@inproceedings {42447,
	title = {Automatic Thumbnail Selection for Soccer Videos using Machine Learning},
	journal = {Proceedings of the 13th ACM Multimedia Systems Conference (MMSys {\textquoteright}22)},
	year = {2022},
	pages = {73-85},
	publisher = {ACM},
	doi = {10.1145/3524273.3528182},
	author = {Husa, Andreas and Midoglu, Cise and Hammou, Malek and Hicks, Steven and Johansen, Dag and Kupka, Tomas and Riegler, Michael and Halvorsen, P{\r a}l}
}
@article {42761,
	title = {Exploration of Different Time Series Models for Soccer Athlete Performance Prediction},
	journal = {MDPI Engineering Proceedings},
	volume = {18},
	year = {2022},
	pages = {37},
	publisher = {MDPI},
	doi = {10.3390/engproc2022018037},
	author = {Kulakou, Siarhei and Ragab, Nourhan and Midoglu, Cise and Boeker, Matthias and Johansen, Dag and Riegler, Michael and Halvorsen, P{\r a}l}
}
@inproceedings {28315,
	title = {Automated Clipping of Soccer Events using Machine Learning},
	journal = {IEEE International Symphosium of Multimedia (ISM)},
	year = {2021},
	month = {12/2021},
	publisher = { IEEE},
	doi = {10.1109/ISM52913.2021.00042},
	author = {Valand, Joakim and Kadragic, Haris and Hicks, Steven and Thambawita, Vajira and Midoglu, Cise and Kupka, Tomas and Johansen, Dag and Riegler, Michael and Halvorsen, P{\r a}l}
}
@article {27774,
	title = {A comprehensive analysis of classification methods in gastrointestinal endoscopy imaging},
	journal = {Medical Image Analysis},
	volume = {70},
	year = {2021},
	pages = {102007},
	publisher = {Elsevier},
	abstract = {Gastrointestinal endoscopy has\&nbsp;been\&nbsp;an\&nbsp;active field\&nbsp;of research motivated by the large number of highly lethal GI cancers. Early GI cancer precursors are often missed during the endoscopic surveillance. \&nbsp; The high missed rate of such abnormalities during endoscopy is thus a\&nbsp; critical bottleneck. Lack of attentiveness due to tiring procedures, and requirement of training are few contributing factors. An automatic\&nbsp; GI\&nbsp; disease classification system can help reduce such risks by flagging suspicious frames and lesions. GI endoscopy consists of several multi-organ surveillance, therefore, there is need to develop methods that can generalize to various endoscopic findings. In this realm, we present a comprehensive analysis of the\&nbsp; Medico\&nbsp; GI\&nbsp; challenges:\&nbsp; Medical\&nbsp; Multimedia Task at MediaEval 2017, Medico Multimedia Task at MediaEval 2018,\&nbsp; and\&nbsp; BioMedia\&nbsp; ACM\&nbsp; MM\&nbsp; Grand\&nbsp; Challenge\&nbsp; 2019.\&nbsp; These challenges are initiative to set-up\&nbsp; a\&nbsp; benchmark for different computer vision methods applied to the multi-class endoscopic images and promote to build new approaches that could reliably be used in clinics. We report the performance of 21 participating teams over a\&nbsp; period of three consecutive years and provide a\&nbsp; detailed analysis of the methods used by the participants, highlighting the challenges and shortcomings of the current approaches and dissect their credibility for the use in clinical settings.\&nbsp; Our analysis revealed that the participants achieved an improvement on maximum\&nbsp; Mathew correlation coefficient\&nbsp; (MCC)\&nbsp; from\&nbsp; 82.68\%\&nbsp; in\&nbsp; 2017\&nbsp; to\&nbsp; 93.98\%\&nbsp; in\&nbsp; 2018\&nbsp; and 95.20\% in 2019 challenges, and a significant increase in computational speed over consecutive years.},
	keywords = {Artificial intelligence, BioMedia 2019 Grand Challenge, Computer-aided detection and diagnosis, Gastrointestinal endoscopy challenges, Medical imaging, Medico Task 2017, Medico Task 2018},
	doi = {https://doi.org/10.1016/j.media.2021.102007},
	author = {Jha, Debesh and Ali, Sharib and Hicks, Steven and Thambawita, Vajira and Borgli, Hanna and Pia H. {Smedsrud} and de Lange, Thomas and Pogorelov, Konstantin and Wang, Xiaowei and Harzig, Philipp and Tran, Minh-Triet and Meng, Wenhua and Hoang, Trung-Hieu and Dias, Danielle and Tobey H. {Ko} and Agrawal, Taruna and Ostroukhova, Olga and Khan, Zeshan and Muhammad Atif {Tahir} and Liu, Yang and Chang, Yuan and Kirker{\o}d, Mathias and Johansen, Dag and Lux, Mathias and H{\r a}vard D. {Johansen} and Riegler, Michael and Halvorsen, P{\r a}l}
}
@article {27640,
	title = {A Comprehensive Study on Colorectal Polyp Segmentation with ResUNet++, Conditional Random Field and Test-Time Augmentation},
	journal = {IEEE Journal of Biomedical and Health Informatics},
	volume = {25},
	year = {2021},
	pages = {2029 - 2040},
	publisher = { IEEE},
	abstract = {Colonoscopy is considered the gold standard for detection of colorectal cancer and its precursors. Existing examination methods are, however, hampered by high overall miss-rate, and many abnormalities are left undetected. Computer-Aided Diagnosis systems based on advanced machine learning algorithms are touted as a game-changer that can identify regions in the colon overlooked by the physicians during endoscopic examinations, and help detect and characterize lesions. In previous work, we have proposed the ResUNet++ architecture and demonstrated that it produces more efficient results compared with its counterparts U-Net and ResUNet. In this paper, we demonstrate that further improvements to the overall prediction performance of the ResUNet++ architecture can be achieved by using Conditional Random Field (CRF) and Test-Time Augmentation (TTA). We have performed extensive evaluations and validated the improvements using six publicly available datasets: Kvasir-SEG, CVC-ClinicDB, CVC-ColonDB, ETIS-Larib PolypDB, ASU-Mayo Clinic Colonoscopy Video Database, and CVC-VideoClinicDB. Moreover, we compare our proposed architecture and resulting model with other State-of-the-art methods. To explore the generalization capability of ResUNet++ on different publicly available polyp datasets, so that it could be used in a real-world setting, we performed an extensive cross-dataset evaluation. The experimental results show that applying CRF andTTA improves the performance on various polyp segmentation datasets both on the same dataset and cross-dataset. To check the model{\textquoteright}s performance on difficult to detect polyps, we selected, with the help of an expert gastroenterologist,196sessile or flat polyps that are less than ten millimeters in size. This additional data has been made available as a subset of Kvasir-SEG. Our approaches showed good results for flat or sessile and smaller polyps, which are known to be one of the major reasons for high polyp miss-rates. This is one of the significant strengths of our work and indicates that our methods should be investigated further for use in clinical practice.},
	keywords = {colonoscopy, conditional random field, generalization, Polyp segmentation, ResUNet++, test-time augmentation},
	doi = {10.1109/JBHI.2021.3049304},
	author = {Jha, Debesh and Pia H. {Smedsrud} and Johansen, Dag and de Lange, Thomas and H{\r a}vard D. {Johansen} and Halvorsen, P{\r a}l and Riegler, Michael}
}
@inproceedings {27635,
	title = {DDANet: Dual Decoder Attention Network for Automatic Polyp Segmentation},
	journal = {25th International Conference on Pattern Recognition},
	year = {2021},
	pages = {307-314},
	publisher = { Springer},
	abstract = {Colonoscopy is the gold standard for examination and detection of colorectal polyps. Localization and delineation of polyps can play a vital role in treatment (e.g., surgical planning) and prognostic decision making. Polyp segmentation can provide detailed boundary information for clinical analysis. Convolutional neural networks have improved the performance in colonoscopy. However, polyps usually possess various challenges, such as intra-and inter-class variation and noise. While manual labeling for polyp assessment requires time from experts and is prone to human error (e.g., missed lesions), an automated, accurate, and fast segmentation can improve the quality of delineated lesion boundaries and reduce missed rate. The Endotect challenge provides an opportunity to benchmark computer vision methods by training on the publicly available Hyperkvasir and testing on a separate unseen dataset. In this paper, we propose a novel architecture called {\textquoteleft}{\textquoteleft}DDANet{\textquoteright}{\textquoteright} based on a dual decoder attention network. Our experiments demonstrate that the model trained on the Kvasir-SEG dataset and tested on an unseen dataset achieves a dice coefficient of 0.7874, mIoU of 0.7010, recall of 0.7987, and a precision of 0.8577, demonstrating the generalization ability of our model.},
	keywords = {Benchmarking, Convolutional neural network, deep learning, Polyp segmentation},
	author = {Nikhil Kumar {Tomar} and Jha, Debesh and Ali, Sharib and H{\r a}vard D. {Johansen} and Johansen, Dag and Riegler, Michael and Halvorsen, P{\r a}l}
}
@inproceedings {27834,
	title = {DeepSynthBody: the beginning of the end for data deficiency in medicine},
	journal = {The International Conference on Applied Artificial Intelligence (ICAPAI)},
	year = {2021},
	publisher = {IEEE},
	abstract = {Limited access to medical data is a barrier on developing new and efficient machine learning solutions in medicine such as computer-aided diagnosis, risk assessments, predicting optimal treatments and home-based personal healthcare systems. This paper presents DeepSynthBody: a novel framework that overcomes some of the inherent restrictions and limitations of medical data by using deep generative adversarial networks to produce synthetic data with characteristics similar to the real data, so-called DeepSynth (deep synthetic) data. We show that DeepSynthBody can address two key issues commonly associated with medical data, namely privacy concerns (as a result of data protection rules and regulations) and the high costs of annotations. To demonstrate the full pipeline of applying DeepSynthBody concepts and user-friendly functionalities, we also describe a synthetic medical dataset generated and published using our framework. DeepSynthBody opens a new era of machine learning applications in medicine with a synthetic model of the human body.},
	doi = {10.1109/ICAPAI49758.2021.9462062},
	author = {Thambawita, Vajira and Hicks, Steven and Isaksen, Jonas and Mette Haug {Stensen} and Trine B {Haugen} and Kanters, J{\o}rgen and Parasa, Sravanthi and de Lange, Thomas and H{\r a}vard D. {Johansen} and Johansen, Dag and Hugo Lewi {Hammer} and Halvorsen, P{\r a}l and Riegler, Michael}
}
@inproceedings {28020,
	title = {Exploring Deep Learning Methods for Real-Time Surgical Instrument Segmentation in Laparoscopy},
	journal = {2021 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI)},
	year = {2021},
	pages = {1-4},
	publisher = {IEEE},
	abstract = {Minimally Invasive Surgery (MIS) is a surgical intervention used to examine the organs inside the abdomen and has been widely used due to its effectiveness over open surgery. Due to the hardware improvements such as high definition cameras, this procedure has significantly improved and new software methods have open potential for computer-assisted procedures. However, there exists challenges and requirement to improve detection and tracking of the position of the instruments during these surgical procedures. To this end, we evaluate and compare some popular deep learning methods that can potentially be explored for the automated segmentation of surgical instruments in laparoscopy, an important step towards tool tracking. Our experimental results demonstrate that the Dual decoder attention network (DDANet) produces a superior result compared to other recent deep learning methods. DDANet produces a dice coefficient of 0.8739 and mean intersection over union of 0.8183 for the Robust Medical Instrument Segmentation (ROBUST-MIS) Challenge 2019 dataset, at a real-time speed of 101.36 frames per second which is critical for such procedures.},
	keywords = {Real-time segmentation, minimally invasive surgery, surgical instruments,  laparoscopy, deep learning},
	author = {Jha, Debesh and Ali, Sharib and Nikhil Kumar {Tomar} and Riegler, Michael and Johansen, Dag and H{\r a}vard D {Johansen} and Halvorsen, P{\r a}l}
}
@inproceedings {27511,
	title = {Kvasir-Instrument: Diagnostic and therapeutic tool segmentation dataset in gastrointestinal endoscopy},
	journal = {27th International Conference on Multimedia Modeling},
	volume = {LNCS, volume 12573},
	year = {2021},
	pages = {218-229},
	publisher = {Springer},
	abstract = {Gastrointestinal (GI) pathologies are periodically screened, biopsied, and resected using surgical tools. Usually the procedures and the treated or resected areas are not specifically tracked or analysed during or after colonoscopies. Information regarding disease borders, development and amount and size of the resected area get lost. This can lead to poor follow-up and bothersome reassessment difficulties post-treatment. To improve the current standard and also to foster more research on the topic we have released the\&nbsp; {\textquoteleft}{\textquoteleft}Kvasir Instrument{\textquoteright}{\textquoteright} dataset which consists of $590$ annotated frames containing GI procedure tools such as snares, balloons and biopsy forceps, etc. Beside of the images, the dataset includes ground truth masks and bounding boxes and has been verified by two expert GI endoscopists. Additionally, we provide a baseline for the segmentation of the GI tools to promote research and algorithm development. We obtained a dice coefficient score of 0.9158 and a Jaccard index of 0.8578 using a classical U-Net architecture. A similar dice coefficient score was observed for DoubleUNet. The qualitative results showed that the model did not work for the images with specularity and the frames with multiple instruments, while the best result for both methods was observed on all other types of images. Both, qualitative and quantitative results show that the model performs reasonably good, but there is a large potential for further improvements.\&nbsp; Benchmarking using the dataset provides an opportunity for researchers to contribute to the field of automatic endoscopic diagnostic and therapeutic tool segmentation for GI endoscopy.\&nbsp;},
	author = {Jha, Debesh and Ali, Sharib and Emanuelsen, Krister and Hicks, Steven and Thambawita, Vajira and Garcia-Ceja, Enrique and Riegler, Michael and de Lange, Thomas and Peter T. {Schmidt} and H{\r a}vard D. {Johansen} and Johansen, Dag and Halvorsen, P{\r a}l}
}
@inproceedings {27856,
	title = {NanoNet: Real-Time Polyp Segmentation in Video Capsule Endoscopy and Colonoscopy},
	journal = {34th IEEE CBMS International Symposium on Computer-Based Medical Systems},
	year = {2021},
	publisher = { IEEE},
	abstract = {Deep learning in gastrointestinal endoscopy can assist to improve clinical performance and be helpful to assess lesions more accurately. To this extent, semantic segmentation methods that can perform automated real-time delineation of a region-of-interest, e.g., boundary identification of cancer or precancerous lesions, can benefit both diagnosis and interventions. However, accurate and real-time segmentation of endoscopic images is extremely challenging due to its high operator dependence and high-definition image quality. To utilize automated methods in clinical settings, it is crucial to design lightweight models with low latency such that they can be integrated with low-end endoscope hardware devices. In this work, we propose NanoNet, a novel architecture for the segmentation of video capsule endoscopy and colonoscopy images. Our proposed architecture allows real-time performance and has higher segmentation accuracy compared to other more complex ones. We use video capsule endoscopy and standard colonoscopy datasets with polyps, and a dataset consisting of endoscopy biopsies and surgical instruments, to evaluate the effectiveness of our approach. Our experiments demonstrate the increased performance of our architecture in terms of a trade-off between model complexity, speed, model parameters, and metric performances. Moreover, the resulting model size is relatively tiny, with only nearly 36,000 parameters compared to traditional deep learning approaches having millions of parameters.},
	keywords = {colonoscopy, deep learning, segmentation, tool  segmentation, Video capsule endoscopy},
	author = {Jha, Debesh and Nikhil Kumar {Tomar} and Ali, Sharib and Riegler, Michael and H{\r a}vard D {Johansen} and Johansen, Dag and de Lange, Thomas and Halvorsen, P{\r a}l}
}
@inproceedings {28229,
	title = {Njord: An out-in-the-wild real world fish vessel catch analysis dataset},
	journal = {Arctic Frontiers},
	year = {2021},
	publisher = {Arctic Frontiers},
	address = {Troms{\o}, Norway},
	author = {Riegler, Michael and Johansen, Dag and Bj{\o}rn Aslak {Juliussen} and Tor-Arne Schmidt {Nordmo} and Aril Bernhard {Ovesen} and Halvorsen, P{\r a}l and H{\r a}vard D. {Johansen} and Jon Petter {Rui}}
}
@article {27773,
	title = {Real-Time Polyp Detection, Localization and Segmentation in Colonoscopy Using Deep Learning},
	journal = {IEEE Access},
	volume = {9},
	year = {2021},
	pages = {40496-40510},
	publisher = { IEEE},
	abstract = {Computer-aided detection, localization, and segmentation methods can help improve colonoscopy procedures. Even though many methods have been built to tackle automatic detection and segmentation of polyps, benchmarking of state-of-the-art methods still remains an open problem. This is due to the increasing number of researched computer vision methods that can be applied to polyp datasets. Benchmarking of novel methods can provide a direction to the development of automated polyp detection and segmentation tasks. Furthermore, it ensures that the produced results in the community are reproducible and provide a fair comparison of developed methods. In this paper, we benchmark several recent state-of-the-art methods using Kvasir-SEG, an open-access dataset of colonoscopy images for polyp detection, localization, and segmentation evaluating both method accuracy and speed. Whilst, most methods in literature have competitive performance over accuracy, we show that the proposed ColonSegNet achieved a better trade-off between an average precision of 0.8000 and mean IoU of 0.8100, and the fastest speed of 180 frames per second for the detection and localization task. Likewise, the proposed ColonSegNetachieved a competitive dice coefficient of 0.8206 and the best average speed of 182.38 frames per second for the segmentation task. Our comprehensive comparison with various state-of-the-art methods reveals the importance of benchmarking the deep learning methods for automated real-time polyp identification and delineations that can potentially transform current clinical practices and minimize miss-detection rates.},
	keywords = {Medical image segmentation,  ColonSegNet, colonoscopy, polyps, deep learning, detection, localization, benchmarking, Kvasir-SEG},
	doi = {10.1109/ACCESS.2021.3063716},
	author = {Jha, Debesh and Ali, Sharib and Nikhil Kumar {Tomar} and H{\r a}vard D {Johansen} and Johansen, Dag and Rittscher, Jens and Riegler, Michael and Halvorsen, P{\r a}l}
}
@inproceedings {27324,
	title = {DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation},
	journal = {CBMS 2020: International Symposium on Computer-Based Medical Systems},
	year = {2020},
	note = {This paper was nominated for the best paper award at CBMS 2020.\&nbsp;},
	publisher = { IEEE},
	author = {Jha, Debesh and Riegler, Michael and Johansen, Dag and Halvorsen, P{\r a}l and H{\r a}vard D {Johansen}}
}
@article {27265,
	title = {An Extensive Study on Cross-Dataset Bias and Evaluation Metrics Interpretation for Machine Learning applied to Gastrointestinal Tract Abnormality Classification},
	journal = {ACM Transactions on Computing for Healthcare},
	volume = {1},
	number = {3},
	year = {2020},
	pages = {1-29},
	publisher = {ACM},
	abstract = {Precise and efficient automated identification of Gastrointestinal (GI) tract diseases can help doctors treat more patients and improve the rate of disease detection and identification. Currently, automatic analysis of diseases in the GI tract is a hot topic in both computer science and medical-related journals. Nevertheless, the evaluation of such an automatic analysis is often incomplete or simply wrong. Algorithms are often only tested on small and biased datasets, and cross-dataset evaluations are rarely performed. A clear understanding of evaluation metrics and machine learning models with cross datasets is crucial to bring research in the field to a new quality level. Towards this goal, we present comprehensive evaluations of five distinct machine learning models using Global Features and Deep Neural Networks that can classify 16 different key types of GI tract conditions, including pathological findings, anatomical landmarks, polyp removal conditions, and normal findings from images captured by common GI tract examination instruments. Inour evaluation, we introduce performance hexagons using six performance metrics such as recall, precision, specificity, accuracy, F1-score, and Matthews Correlation Coefficient to demonstrate how to determine the real capabilities of models rather than evaluatingthem shallowly. Furthermore, we perform cross-dataset evaluations using different datasets for training and testing. With these cross-dataset evaluations, we demonstrate the challenge of actually building a generalizable model that could be used across different hospitals. Our experiments clearly show that more sophisticated performance metrics and evaluation methods need to be applied to get reliable models rather than depending on evaluations of the splits of the same dataset, i.e., the performance metrics should always be interpreted together rather than relying on a single metric.},
	author = {Thambawita, Vajira and Jha, Debesh and Hugo Lewi {Hammer} and H{\r a}vard D {Johansen} and Johansen, Dag and Halvorsen, P{\r a}l and Riegler, Michael}
}
@inproceedings {39534,
	title = {Kvasir-instrument: Diagnostic and therapeutic tool segmentation dataset in gastrointestinal endoscopy},
	journal = {International Conference on Multimedia Modeling},
	year = {2020},
	publisher = { Springer},
	abstract = {Gastrointestinal\&nbsp;pathologies are periodically screened, biopsied, and resected using surgical tools. Usually, the procedures and the treated or resected areas are not specifically tracked or analysed during or after colonoscopies. Information regarding disease borders, development,\&nbsp; amount, and size of the resected area get lost. This can lead to poor follow-up and bothersome reassessment difficulties post-treatment. To improve the current standard and also to foster more research on the topic, we have released the\&nbsp; {\textquoteleft}{\textquoteleft}Kvasir-Instrument{\textquoteright}{\textquoteright} dataset, which consists of 590\&nbsp;annotated frames containing GI\&nbsp;procedure tools such as snares, balloons, and biopsy forceps, etc. Besides the images, the dataset includes ground truth masks and bounding boxes and has been verified by two expert GI\&nbsp;endoscopists. Additionally, we provide a baseline for the segmentation of the GI tools to promote research and algorithm development. We obtained a dice coefficient score of 0.9158\&nbsp;and a Jaccard index of 0.8578\&nbsp;using a classical U-Net architecture. A similar dice coefficient score was observed for DoubleUNet. The qualitative results showed that the model did not work for the images with specularity and the frames with multiple tools, while the best result for both methods was observed on all other types of images. Both qualitative and quantitative results show that the model performs reasonably good, but there is potential for further improvements.\&nbsp; Benchmarking using the dataset provides an opportunity for researchers to contribute to the field of automatic endoscopic diagnostic and therapeutic tool segmentation for GI\&nbsp;endoscopy.},
	keywords = {Benchmarking, Convolutional  neural network, Gastrointestinal endoscopy, Tool segmentation},
	url = {https://www.springerprofessional.de/en/kvasir-instrument-diagnostic-and-therapeutic-tool-segmentation-d/18788368},
	author = {Jha, Debesh and Ali, Sharib and Emanuelsen, Krister and Hicks, Steven and Thambawita, Vajira and Garcia-Ceja, Enrique and Riegler, Michael and de Lange, Thomas and Peter T. {Schmidt} and H{\r a}vard D {Johansen} and Johansen, Dag and Halvorsen, P{\r a}l}
}
@inproceedings {26811,
	title = {Kvasir-SEG: A Segmented Polyp Dataset},
	journal = {International Conference on Multimedia Modeling},
	year = {2020},
	pages = {451-462},
	publisher = {Springer},
	address = {Daejeon, Korea},
	abstract = {Pixel-wise image segmentation is a highly demanding task in medical image analysis. In practice, it is difficult to find annotated medical images with corresponding segmentation masks. In this paper, we present\&nbsp; Kvasir-SEG: an open-access dataset of gastrointestinal polyp images and corresponding segmentation masks, manually annotated and verified by an experienced gastroenterologist. We demonstrate the use of our dataset with a traditional segmentation approach and a modern deep learning based CNN approach. This work will be valuable for researchers to reproduce results and compare their methods in the future.\&nbsp;By adding segmentation masks to the Kvasir dataset, which until today only consisted of framewise annotations, we enable multimedia and computer vision researchers to contribute in the field of polyp segmentation and automatic analysis of colonoscopy videos.\&nbsp;\&nbsp;},
	keywords = {Kvasir-SEG dataset, Medical images, Polyp segmentation, ResUNet  Fuzzy c-mean clustering, Semantic segmentation},
	author = {Jha, Debesh and Pia H. {Smedsrud} and Riegler, Michael and Halvorsen, P{\r a}l and H{\r a}vard D. {Johansen} and de Lange, Thomas and Johansen, Dag}
}
@inproceedings {27639,
	title = {LightLayers: Parameter Efficient Dense and Convolutional Layers for Image Classification},
	journal = {The Joint International Conference PDCAT-PAAP 2020},
	year = {2020},
	publisher = { Springer},
	abstract = {Deep Neural Networks (DNNs) have become the de-facto standard in computer vision, as well as in many other pattern recognition tasks. A key drawback of DNNs is that the training phase can be very computationally expensive. Organizations or individuals that cannot afford purchasing state-of-the-art hardware or tapping into cloud-hosted infrastructures may face a long waiting time before the training completes or might not be able to train a model at all. Investigating novel ways to reduce the training time could be a potential solution to alleviate this drawback, and thus enabling more rapid development of new algorithms and models. In this paper, we propose LightLayers, a method for reducing the number of trainable parameters in deep neural networks (DNN). The proposed LightLayers consists of LightDense andLightConv2D layer that are as efficient as regular Conv2D and Dense layers, but uses less parameters. We resort to Matrix Factorization to reduce the complexity of the DNN models resulting into lightweight DNNmodels that require less computational power, without much loss in the accuracy. We have tested LightLayers on MNIST, Fashion MNIST, CI-FAR 10, and CIFAR 100 datasets. Promising results are obtained for MNIST, Fashion MNIST, CIFAR-10 datasets whereas CIFAR 100 shows acceptable performance by using fewer parameters.},
	keywords = {CIFAR-10, Convolutional  neural network, Deep  Learning, Fashion MNIST, Lightweight  model, MNIST, Weight decomposition},
	author = {Jha, Debesh and Yazidi, Anis and Riegler, Michael and Johansen, Dag and H{\r a}vard D. {Johansen} and Halvorsen, P{\r a}l}
}
@inproceedings {27637,
	title = {Medico Multimedia Task at MediaEval 2020: Automatic Polyp Segmentation},
	journal = {Medico MediaEval 2020},
	year = {2020},
	publisher = {CEUR},
	abstract = {Colorectal cancer is the third most common cause of cancer worldwide. According to Global cancer statistics 2018, the incidence of colorectal cancer is increasing in both developing and developed countries. Early detection of colon anomalies such as polyps is important for cancer prevention, and automatic polyp segmentation can play a crucial role for this. Regardless of the recent advancement in early detection and treatment options, the estimated polyp miss rate is still around 20\%. Support via an automated computer-aided diagnosis system could be one of the potential solutions for the overlooked polyps. Such detection systems can help low-cost design solutions and save doctors time, which they could for example use to perform more patient examinations. In this paper, we introduce the 2020 Medico challenge, provide some information on related work and the dataset, describe the task and evaluation metrics, and discuss the necessity of organizing the Medico challenge.},
	author = {Jha, Debesh and Hicks, Steven and Emanuelsen, Krister and H{\r a}vard D. {Johansen} and Johansen, Dag and de Lange, Thomas and Riegler, Michael and Halvorsen, P{\r a}l}
}
@inproceedings {27333,
	title = {PMData: a sports logging dataset},
	journal = {Proceedings of the 11th ACM Multimedia Systems Conference},
	year = {2020},
	pages = {231-236},
	publisher = {ACM},
	author = {Thambawita, Vajira and Hicks, Steven and Borgli, Hanna and H{\r a}kon Kvale {Stensland} and Jha, Debesh and Martin Kristoffer {Svensen} and Pettersen, Svein-Arne and Johansen, Dag and H{\r a}vard Dagenborg {Johansen} and Susann Dahl {Pettersen} and Nordvang, Simon and Pedersen, Sigurd and Gjerdrum, Anders and Gr{\o}nli, Tor-Morten and Per Morten {Fredriksen} and Eg, Ragnhild and Hansen, Kjeld and Fagernes, Siri and Claudi, Christine and Bi{\o}rn-Hansen, Andreas and Duc Tien Dang {Nguyen} and Kupka, Tomas and Hugo Lewi {Hammer} and Jain, Ramesh and Riegler, Michael and Halvorsen, P{\r a}l}
}
@inproceedings {27557,
	title = {Real-Time Detection of Events in Soccer Videos using 3D Convolutional Neural Networks},
	journal = {2020 IEEE International Symposium on Multimedia (ISM)},
	year = {2020},
	publisher = {IEEE},
	abstract = {In this paper, we present an algorithm for automatically detecting events in soccer videos using 3D convolutional neural networks. The algorithm uses a sliding window approach to scan over a given video to detect events such as goals, yellow/red cards, and player substitutions. We test the method on three different datasets from SoccerNet, the Swedish\&nbsp; Allsvenskan, and the Norwegian Eliteserien. Overall, the results show that we can detect events with high recall, low latency, and accurate time estimation. The trade-off is a slightly lower precision compared to the current state-of-the-art, which has higher latency and performs better when a less accurate time estimation can be accepted. In addition to the presented algorithm, we perform an extensive ablation study on how the different parts of the training pipeline affect the final results.\&nbsp;},
	doi = {10.1109/ISM.2020.00030},
	author = {Olav A. Norg{\r a}r {Rongved} and Hicks, Steven and Thambawita, Vajira and H{\r a}kon Kvale {Stensland} and Zouganeli, Evi and Johansen, Dag and Riegler, Michael and Halvorsen, P{\r a}l}
}
@article {26631,
	title = {Efficient Live and On-Demand Tiled HEVC 360 VR Video Streaming},
	journal = {International Journal of Semantic Computing},
	volume = {13},
	number = {3},
	year = {2019},
	pages = {367-391},
	publisher = {World Scientific},
	abstract = {360\&nbsp;panorama video displayed through Virtual reality (VR) glasses or large screens o{\textregistered}ers immersive user experiences, but as such technology becomes commonplace, the need for e{\textpm}cient streaming methods of such high-bitrate videos arises. In this respect, the attention that 360panorama video has received lately is huge. Many methods have already been proposed, and in this paper, we shed more light on the di{\textregistered}erent trade-o{\textregistered}s in order to save bandwidth while preserving the video quality in the user{\textquoteright}s ̄eld-of-view (FoV). Using 360\&nbsp;VR content delivered to a Gear VR head-mounted display with a Samsung Galaxy S7 and to a Huawei Q22 set-top- box, we have tested various tiling schemes analyzing the tile layout, the tiling and encoding overheads, mechanisms for faster quality switching beyond the DASH segment boundaries and quality selection con ̄gurations. In this paper, we present an e{\textpm}cient end-to-end design and real-world implementation of such a 360\&nbsp;streaming system. Furthermore, in addition to researching an on-demand system, we also go beyond the existing on-demand solutions and present a\&nbsp;live\&nbsp;streaming system which strikes a trade-o{\textregistered} between bandwidth usage and the video quality in the user{\textquoteright}s FoV. We have created an architecture that combines RTP and DASH, and our system multiplexes a single HEVC hardware decoder to provide faster quality switching than at the traditional GOP boundaries. We demonstrate the performance and illustrate the trade-o{\textregistered}s through real-world experiments where we can report comparable bandwidth savings to existing on-demand approaches, but with faster quality switches when the FoV changes.},
	author = {Jeppsson, Mattis and Espeland, H{\r a}vard and Kupka, Tomas and Langseth, Ragnar and Petlund, Andreas and Qiaoqiao, Peng and Xue, Chuansong and Johansen, Dag and Pogorelov, Konstantin and H{\r a}kon Kvale {Stensland} and Griwodz, Carsten and Riegler, Michael and Halvorsen, P{\r a}l}
}
@inproceedings {26589,
	title = {Performance of Data Enhancements and Training Optimization for Neural Network {\textendash} A Polyp Detection Case Study},
	journal = {IEEE CBMS International Symposium on Computer-Based Medical Systems (CBMS)},
	year = {2019},
	publisher = { IEEE},
	abstract = {Deep learning using neural networks is becoming more and more popular. It is frequently used in areas like video analysis, image retrieval, traffic forecast and speech recognition. In this respect, the learning and training process usually requires a lot of data. However, in many areas, data is scarce which is definitely the case in our medical application scenario, i.e., polyp detection in the gastrointestinal tract. Here, colorectal cancer is on the list of most common cancer types, and often, the cancer arises from benign, adenomatous polyps containing dysplastic cells. Detection and removal of polyps can therefore prevent the development of cancer. Due to high cost, time consumption, patient discomfort and in-accuracy of existing procedures, researchers have started to explore systems for automatic polyp detection to assist and automate current examination procedures. Following the current gained traction for neural networks, and the typical lack of medical data, we explore how data enhancements affect the training and evaluation of the networks in terms of polyp detection accuracy and particularly if it can be used to increase the detection rate. We also experiment with how various training techniques can be used to increase performance. Our experimental results show how data enhancement and training optimization can be used to increase different aspects of the performance, but we also point out mechanisms that have no and even a negative effect.},
	author = {Fredrik Lund {Henriksen} and Jensen, Rune and H{\r a}kon Kvale {Stensland} and Johansen, Dag and Riegler, Michael and Halvorsen, P{\r a}l}
}
@inproceedings {26630,
	title = {Predicting Peek Readiness-to-Train of Soccer Players Using Long Short-Term Memory Recurrent Neural Networks},
	journal = {2019 International Conference on Content-Based Multimedia Indexing (CBMI)},
	year = {2019},
	publisher = {IEEE},
	abstract = {We are witnessing the emergence of a myriad of hardware and software systems that quantifies sport and physical activities. These are frequently touted as game changers and important for future sport developments. The vast amount of generated data is often visualized in graphs and dashboards, for use by coaches and other sports professionals to make decisions on training and match strategies. Modern machine- learning methods have the potential to further fuel this process by deriving useful insights that are not easily observable in the raw data streams.This paper tackles the problem of deriving peaks in soccer players{\textquoteright} ability to perform from subjective self-reported wellness data collected using the PMSys system. For this, we train a long short-term memory recurrent neural network model using data from two professional Norwegian soccer teams. We show that our model can predict performance peaks in most scenarios with a precision and recall of at least 90\%. Equipped with such insight, coaches and trainers can better plan individual and team training sessions, and perhaps avoid over training and injuries.},
	doi = {10.1109/CBMI.2019.8877406},
	author = {Wiik, Theodor and H{\r a}vard D {Johansen} and Svein Arne {Pettersen} and Baptista, Ivan and Kupka, Tomas and Johansen, Dag and Riegler, Michael and Halvorsen, P{\r a}l}
}
@inproceedings {26629,
	title = {Real-time Analysis of Physical Performance Parameters in Elite Soccer},
	journal = {2019 International Conference on Content-Based Multimedia Indexing (CBMI)},
	year = {2019},
	publisher = { IEEE},
	abstract = {Technology is having vast impact on the sports in- dustry, and in particular soccer. All over the world, soccer teams are adapting digital information systems to quantify performance metrics. The goal is to assess strengths and weaknesses of indi- vidual players, training regimes, and play strategies; to improve performance and win games. However, most existing methods rely on post-game analytic. This limits coaches to review games in retrospect without any means to do changes during sessions. In collaboration with an elite soccer club, we have developed Metrix which is a computerized toolkit for coaches to perform real- time monitoring and analysis of the players{\textquoteright} performance. Using sensor technology to track movement, performance parameters are instantly available to coaches through a mobile phone client. Metrix provides coaches with a toolkit to individualize training load to different playing positions on the field, or to the player himself. Our results show that Metrix is able to quantify player performance and propagate it to coaches in real-time during a match or practice, i.e., latency is below 100 ms on the field. In our initial user evaluation, the coaches express that this is a valuable asset in day-to-day work.},
	doi = {10.1109/CBMI.2019.8877422},
	author = {Andreassen, Kim and Johansen, Dag and Johansen, H{\r a}vard and Baptista, Ivan and Svein Arne {Pettersen} and Riegler, Michael and Halvorsen, P{\r a}l}
}
@inproceedings {26812,
	title = {ResUNet++: An Advanced Architecture for Medical Image Segmentation},
	journal = {2019 IEEE International Symposium on Multimedia (ISM)},
	year = {2019},
	publisher = {IEEE},
	address = {San Diego, California, USA},
	abstract = {Accurate computer-aided polyp detection and segmentation during colonoscopy examinations can help endoscopists resect abnormal tissue and thereby decrease chances of polyps growing into cancer. Towards developing a fully automated model for pixel-wise polyp segmentation, we propose ResUNet++, which is an improved ResUNet architecture for colonoscopic image segmentation. Our experimental evaluations show that the suggested architecture produces good segmentation results on publicly available datasets. Furthermore, ResUNet++ significantly outperforms U-Net and ResUNet, two key state-of-the-art deep learning architectures, by achieving high evaluation scores with a dice coefficient of 81.33\%, and a mean Intersection over Union (mIoU) of 79.27\% for the Kvasir-SEG dataset and a dice coefficient of 79.55\%, and a mIoU of 79.62\% with CVC-612 dataset.},
	keywords = {colonoscopy, deep learning, health informatics, Medical image segmentation, Polyp segmentation, Semantic segmentation},
	author = {Jha, Debesh and Pia H. {Smedsrud} and Riegler, Michael and Johansen, Dag and de Lange, Thomas and Halvorsen, P{\r a}l and H{\r a}vard D {Johansen}}
}
@inproceedings {26708,
	title = {Semantic Analysis of Soccer News for Automatic Game Event Classification},
	journal = {2019 International Conference on Content-Based Multimedia Indexing (CBMI)},
	year = {2019},
	publisher = {IEEE},
	abstract = {We are today overwhelmed with information, of which an important part is news. Sports news, in particular, has become very popular, where soccer makes up a big part of this coverage. For sports fans, it can be a time consuming and tedious to keep up with the news that they really care about. In this paper, we present different machine learning methods applied to soccer news from a Norwegian newspaper and a TV station{\textquoteright}s news site to summarize the content in a short and digestible manner. We present a system to collect, index, label, analyze, and present the collected news articles based on the content. We perform a thorough comparison between deep learning and traditional machine learning algorithms on text classification. Furthermore, we present a dataset of soccer news which was collected from two different Norwegian news sites and shared online.\&nbsp;},
	author = {Nordskog, Aanund and Halvorsen, P{\r a}l and Hicks, Steven and H{\r a}kon Kvale {Stensland} and Hugo Lewi {Hammer} and Johansen, Dag and Riegler, Michael}
}
@inbook {25869,
	title = {Camera Synchronization for Panoramic Videos},
	booktitle = {MediaSync},
	year = {2018},
	month = {03/2018},
	pages = {565-592},
	publisher = {Springer},
	organization = {Springer},
	doi = {10.1007/978-3-319-65840-7_20},
	url = {https://doi.org/10.1007/978-3-319-65840-7_20},
	author = {Vamsidhar Reddy {Gaddam} and Langseth, Ragnar and H{\r a}kon Kvale {Stensland} and Griwodz, Carsten and Riegler, Michael and Kupka, Tomas and Espeland, H{\r a}vard and Johansen, Dag and Johansen, H{\r a}vard and Halvorsen, P{\r a}l},
	editor = {Montagud, Mario and Cesar, Pablo and Boronat, Fernando and Jansen, Jack}
}
@inproceedings {25954,
	title = {Deep Learning and Hand-crafted Feature Based Approaches for Polyp Detection in Medical Videos},
	journal = {31st IEEE CBMS International Symposium on Computer-Based Medical Systems},
	year = {2018},
	pages = {381-386},
	publisher = {IEEE},
	address = {Karlstad, Sweden},
	issn = {2372-9198},
	doi = {10.1109/CBMS.2018.00073},
	author = {Pogorelov, Konstantin and Ostroukhova, Olga and Jeppsson, Mattis and Espeland, H{\r a}vard and Griwodz, Carsten and de Lange, Thomas and Johansen, Dag and Riegler, Michael and Halvorsen, P{\r a}l}
}
@inproceedings {26250,
	title = {Efficient Live and on-Demand Tiled HEVC 360 VR Video Streaming},
	journal = {2018 IEEE International Symposium on Multimedia (ISM)},
	year = {2018},
	month = {12/2018},
	pages = {81-88},
	publisher = {IEEE},
	address = {Taichung, Taiwan},
	abstract = {With 360{\textopenbullet} panorama video technology becoming commonplace, the need for efficient streaming methods for such videos arises. We go beyond the existing on-demand solutions and present a live streaming system which strikes a trade-off between bandwidth usage and the video quality in the user{\textquoteright}s field-of-view. We have created an architecture that combines RTP and DASH to deliver 360{\textopenbullet} VR content to a Huawei set-top-box and a Samsung Galaxy S7. Our system multiplexes a single HEVC hardware decoder to provide faster quality switching than at the traditional GOP boundaries. We demonstrate the performance and illustrate the trade-offs through real-world experiments where we can report comparable bandwidth savings to existing on-demand approaches, but with faster quality switches when the field-of- view changes.\&nbsp;},
	doi = {10.1109/ISM.2018.00022},
	author = {Jeppsson, Mattis and Espeland, H{\r a}vard and Kupka, Tomas and Langseth, Ragnar and Petlund, Andreas and Qiaoqiao, Peng and Xue, Chuansong and Pogorelov, Konstantin and Riegler, Michael and Johansen, Dag and Griwodz, Carsten and Halvorsen, P{\r a}l}
}
@inproceedings {26205,
	title = {The Medico-Task 2018: Disease Detection in the Gastrointestinal Tract using Global Features and Deep Learning},
	journal = {MediaEval 2018},
	year = {2018},
	month = {10/2018},
	publisher = {MediaEval},
	address = {Nice, France},
	abstract = {In this paper, we present our approach for the 2018 Medico Task classifying diseases in the gastrointestinal tract. We have proposed a system based on global features and deep neural networks. The best approach combines two neural networks and the reproducible experimental results signify the efficiency of the proposed model with an accuracy rate of 95.80\%, a precision of 95.87\%, and an F1-score of 95.80\%.},
	keywords = {CNN, deep learning, Gastrointestinal Disease Detection, Global Features, Medico-Task 2018, Transfer Learning},
	author = {Thambawita, Vajira and Jha, Debesh and Riegler, Michael and Halvorsen, P{\r a}l and Hugo Lewi {Hammer} and H{\r a}vard D. {Johansen} and Johansen, Dag}
}
@inproceedings {25951,
	title = {OpenSea - Open Search Based Classification Tool},
	journal = {Proceedings of the 9th ACM Multimedia Systems Conference},
	year = {2018},
	pages = {363-368},
	publisher = {ACM},
	address = {Amsterdam, Netherlands},
	isbn = {978-1-4503-5192-8},
	doi = {10.1145/3204949.3208128},
	author = {Pogorelov, Konstantin and Albisser, Zeno and Ostroukhova, Olga and Lux, Mathias and Johansen, Dag and Halvorsen, P{\r a}l and Riegler, Michael}
}
@article {25462,
	title = {Efficient disease detection in gastrointestinal videos {\textendash} global features versus neural networks},
	journal = {Multimedia Tools and Applications},
	volume = {76},
	year = {2017},
	month = {11/2017},
	pages = {22493{\textendash}22525},
	publisher = {ACM/Springer},
	abstract = {Analysis of medical videos from the human gastrointestinal (GI) tract for detection and localization of abnormalities like lesions and diseases requires both high precision and recall. Additionally, it is important to support efficient, real-time processing for live feedback during (i) standard colonoscopies and (ii) scalability for massive population-based screening, which we conjecture can be done using a wireless video capsule endoscope (camera-pill). Existing related work in this field does neither provide the necessary combination of accuracy and performance for detecting multiple classes of abnormalities simultaneously nor for particular disease localization tasks. In this paper, a complete end-to-end multimedia system is presented where the aim is to tackle automatic analysis of GI tract videos. The system includes an entire pipeline ranging from data collection, processing and analysis, to visualization. The system combines deep learning neural networks, information retrieval, and analysis of global and local image features in order to implement multi-class classification, detection and localization. Furthermore, it is built in a modular way, so that it can be easily extended to deal with other types of abnormalities. Simultaneously, the system is developed for efficient processing in order to provide real-time feedback to the doctors and for scalability reasons when potentially applied for massive population-based algorithmic screenings in the future. Initial experiments show that our system has multiclass detection accuracy and polyp localization precision at least as good as state-of-the-art systems, and provides additional novelty in terms of real-time performance, low resource consumption and ability to extend with support for new classes of diseases.},
	keywords = {Algorithmic screening, Automatic disease detection, Deep learning neural networks, Global and local image features, Information retrieval, Medical, performance evaluation},
	issn = {1380-7501},
	doi = {10.1007/s11042-017-4989-y},
	url = {https://link.springer.com/article/10.1007\%2Fs11042-017-4989-y},
	author = {Pogorelov, Konstantin and Riegler, Michael and Sigrun Losada {Eskeland} and de Lange, Thomas and Johansen, Dag and Griwodz, Carsten and Peter Thelin {Schmidt} and Halvorsen, P{\r a}l}
}
@inproceedings {25338,
	title = {EIR: changing the scene of automatic detection software for gastrointestinal endoscopy},
	journal = {World Congress of GI Endoscopy},
	year = {2017},
	month = {02/2017},
	publisher = {World Endoscopic Organisation},
	address = {Hyderabad, India},
	author = {Sigrun L. {Eskeland} and Aabakken, Lars and Riegler, Michael and Pogorelov, Konstantin and Peter Thelin {Schmidt} and Johansen, Dag and Halvorsen, P{\r a}l},
	editor = {de Lange, Thomas}
}
@article {25265,
	title = {From Annotation to Computer-Aided Diagnosis: Detailed Evaluation of a Medical Multimedia System},
	journal = {ACM Transactions on Multimedia Computing, Communications, and Applications},
	volume = {13},
	year = {2017},
	publisher = {ACM},
	author = {Riegler, Michael and Pogorelov, Konstantin and Sigrun L. {Eskeland} and Peter Thelin {Schmidt} and Albisser, Zeno and Johansen, Dag and Griwodz, Carsten and Halvorsen, P{\r a}l and de Lange, Thomas}
}
@inproceedings {25433,
	title = {A Holistic Multimedia System for Gastrointestinal Tract Disease Detection},
	journal = {8th annual ACM conference on Multimedia Systems (MMSys)},
	year = {2017},
	month = {06/2017},
	pages = {112-123},
	publisher = {ACM},
	abstract = {Analysis of medical videos for detection of abnormalities and diseases requires both high precision and recall, but also real-time processing for live feedback and scalability for massive screening of entire populations. Existing work on this field does not provide the necessary combination of retrieval accuracy and performance.\&nbsp;In this paper, a multimedia system is presented where the aim is to tackle automatic analysis of videos from the human gastrointestinal (GI) tract. The system includes the whole pipeline from data collection, processing and analysis, to visualization. The system combines filters using machine learning, image recognition and extraction of global and local image features. Furthermore, it is built in a modular way so that it can easily be extended. At the same time, it is developed for efficient processing in order to provide real-time feedback to the doctors. Our experimental evaluation proves that our system has detection and localisation accuracy at least as good as existing systems for polyp detection, it is capable of detecting a wider range of diseases, it can analyze video in real-time, and it has a low resource consumption for scalability.},
	isbn = {978-1-4503-5002-0},
	doi = {10.1145/3083187.3083189},
	url = {http://dl.acm.org/citation.cfm?id=3083189},
	author = {Pogorelov, Konstantin and Sigrun Losadal {Eskeland} and de Lange, Thomas and Griwodz, Carsten and Kristin Ranheim {Randel} and H{\r a}kon Kvale {Stensland} and Dang-Nguyen, Duc-Tien and Spampinato, Concetto and Johansen, Dag and Riegler, Michael and Halvorsen, P{\r a}l},
	editor = {Cesar, Pablo and Hsu, Cheng-hsin}
}
@inproceedings {25332,
	title = {Kvasir: A Multi-Class Image-Dataset for Computer Aided Gastrointestinal Disease Detection},
	journal = {ACM Multimedia Systems},
	year = {2017},
	month = {07/2017},
	publisher = {ACM},
	address = {Taiwan},
	author = {Pogorelov, Konstantin and Ranheim, Kristin and Griwodz, Carsten and de Lange, Thomas and Sigrun L. {Eskeland} and Johansen, Dag and Peter Thelin {Schmidt} and Spampinato, Concetto and Dang-Nguyen, Duc-Tien and Lux, Mathias and Riegler, Michael and Halvorsen, P{\r a}l}
}
@inproceedings {25333,
	title = {Nerthus: A Bowel Preparation Quality Video Dataset},
	journal = {ACM Multimedia Systems},
	year = {2017},
	publisher = {ACM},
	address = {Taipei},
	author = {Pogorelov, Konstantin and Ranheim, Kristin and de Lange, Thomas and Sigrun L. {Eskeland} and Johansen, Dag and Griwodz, Carsten and Spampinato, Concetto and Taschwer, Mario and Lux, Mathias and Peter Thelin {Schmidt} and Riegler, Michael and Halvorsen, P{\r a}l}
}
@inproceedings {24472,
	title = {Computer Aided Disease Detection System for Gastrointestinal Examinations},
	journal = {Multimedia Systems Conference 2016},
	year = {2016},
	month = {05/2016},
	publisher = {ACM},
	address = {New York},
	author = {Riegler, Michael and Pogorelov, Konstantin and Markussen, Jonas and Lux, Mathias and H{\r a}kon Kvale {Stensland} and de Lange, Thomas and Griwodz, Carsten and Halvorsen, P{\r a}l and Johansen, Dag and Peter Thelin {Schmidt} and Sigrun L. {Eskeland}}
}
@inproceedings {24482,
	title = {EIR - Efficient Computer Aided Diagnosis Framework for Gastrointestinal Endoscopies},
	journal = {International Workshop on Content-based Multimedia Indexing},
	year = {2016},
	month = {06/2016},
	publisher = {IEEE / ACM},
	author = {Riegler, Michael and Pogorelov, Konstantin and Halvorsen, P{\r a}l and de Lange, Thomas and Griwodz, Carsten and Johansen, Dag and Peter Thelin {Schmidt} and Sigrun L. {Eskeland}}
}
@inproceedings {24478,
	title = {GPU-accelerated Real-time Gastrointestinal Diseases Detection},
	journal = {CBMS 2016 : The 29th International Symposium on Computer-Based Medical Systems},
	year = {2016},
	month = {07/2016},
	publisher = {IEEE},
	author = {Pogorelov, Konstantin and Riegler, Michael and Halvorsen, P{\r a}l and de Lange, Thomas and Peter Thelin {Schmidt} and Sigrun L. {Eskeland} and Griwodz, Carsten and Johansen, Dag}
}
@inproceedings {24641,
	title = {Multimedia and Medicine: Teammates for Better Disease Detection and Survival},
	journal = {ACM Multimedia},
	year = {2016},
	month = {10/2016},
	publisher = {ACM},
	address = {Amsterdam, The Netherlands, The Netherlands},
	doi = {http://dx.doi.org/10.1145/2964284.2976760},
	author = {Riegler, Michael and Griwodz, Carsten and Spampinato, Concetto and de Lange, Thomas and Sigrun L. {Eskeland} and Pogorelov, Konstantin and Tavanapong, Wallapak and Peter Thelin {Schmidt} and Gurrin, Cathal and Johansen, Dag and Johansen, H{\r a}vard and Halvorsen, P{\r a}l}
}
@article {23460,
	title = {An Experimental Evaluation of Debayering Algorithms on GPUs for Recording Panoramic Video in Real-time},
	journal = {International Journal of Multimedia Data Engineering and Management (IJMDEM)},
	volume = {6},
	year = {2015},
	month = {07/2015},
	pages = {1-16},
	publisher = {IGI Global},
	abstract = {Modern video cameras often only capture a single color per pixel\&nbsp;in a single pass operation. This process is called ltering, where pixels\&nbsp;are ltered through a color lter array, and the Bayer filter is perhaps\&nbsp;the most common filter used today. This means that we must restore\&nbsp;the missing color channels in the image or the video frame in a post-processing step, i.e., a process referred to as debayering. In a live\&nbsp;video scenario, this operation must be performed eciently in order\&nbsp;to output each video frame in real-time, while also yielding acceptable\&nbsp;visual quality. Here, we evaluate debayering algorithms implemented\&nbsp;on a GPU for real-time panoramic video recordings using multiple\&nbsp;2K-resolution cameras.},
	issn = {1947-8534},
	doi = {10.4018/ijmdem.2015070101},
	author = {Langseth, Ragnar and Vamsidhar Reddy {Gaddam} and H{\r a}kon Kvale {Stensland} and Griwodz, Carsten and Halvorsen, P{\r a}l and Johansen, Dag}
}
@inproceedings {23370,
	title = {Scaling Virtual Camera Services to a Large Number of Users},
	journal = {Proceedings of the 6th annual ACM conference on Multimedia Systems (MMSYS)},
	year = {2015},
	month = {03/2015},
	pages = {93-96},
	publisher = {ACM},
	address = {New York, NY, USA},
	abstract = {By processing video footage from a camera array, one can easily make\&nbsp;wide-field-of-view panorama videos. From the single panorama video,\&nbsp;one can further generate multiple virtual cameras supporting\&nbsp;personalized views to a large number of users based on only the few\&nbsp;physical cameras in the array. However, giving personalized services to large\&nbsp;numbers of users potentially introduces both bandwidth and processing\&nbsp;bottlenecks, depending on where the virtual camera is processed.In this demonstration, we present a system that address the large cost\&nbsp;of transmitting entire panorama video to the end-user where the user\&nbsp;creates the virtual views on the client device. Our approach is to\&nbsp;divide the panorama into tiles, each encoded in multiple\&nbsp;qualities. Then, the panorama video tiles are retrieved by the client\&nbsp;in a quality (and thus bit rate) depending on where the virtual camera\&nbsp;is pointing, i.e., the video quality of the tile changes dynamically\&nbsp;according to the user interaction. Our initial experiments indicate\&nbsp;that there is a large potential of saving bandwidth on the cost of\&nbsp;trading quality of in areas of the panorama frame not used for the\&nbsp;extraction of the virtual view.\&nbsp;},
	isbn = { 978-1-4503-3351-1},
	author = {Vamsidhar Reddy {Gaddam} and Langseth, Ragnar and H{\r a}kon Kvale {Stensland} and Griwodz, Carsten and Johansen, Dag and Halvorsen, P{\r a}l}
}
@inproceedings {23535,
	title = {Tiling of Panorama Video for Interactive Virtual Cameras: Overheads and Potential Bandwidth Requirement Reduction},
	journal = {21st International Packet Video Workshop (PV 2015)},
	year = {2015},
	month = {06/2015},
	publisher = {IEEE},
	abstract = {Delivering high resolution, high bitrate panoramavideo to a large number of users introduces huge scalingchallenges. To reduce the resource requirement, researchers haveearlier proposed tiling in order to deliver different qualities indifferent spatial parts of the video. In our work, providing aninteractive moving virtual camera to each user, tiling may be usedto reduce the quality depending on the position of the virtualview. This raises new challenges compared to existing tilingapproaches as the need for high quality tiles dynamically change.In this paper, we describe a tiling approach of panorama videofor interactive virtual cameras where we provide initial resultsshowing the introduced overheads and the potential reduction inbandwidth requirement.\&nbsp;},
	author = {Vamsidhar Reddy {Gaddam} and Hoang Bao {Ngo} and Langseth, Ragnar and Griwodz, Carsten and Johansen, Dag and Halvorsen, P{\r a}l}
}
@inproceedings {mmsys.2014-02-12.3999932578,
	title = {Automatic Event Extraction and Video Summaries From Soccer Games},
	journal = {Proceedings of the 5th annual ACM conference on Multimedia Systems (MMSYS)},
	year = {2014},
	month = {March},
	publisher = {ACM},
	type = {Conference},
	abstract = {Bagadus is a prototype of a soccer analysis application which integrates a sensor system, a video camera array and soccer analytics annotations. The current prototype is installed at Alfheim Stadium in Norway, and provides a large set of new functions compared to existing solutions. One important feature is to automatically extract video events and sum- maries from the games, i.e., an operation that traditionally consumes a huge amount of time. In this demo, we demon- strate how our integration of subsystems enable several types of summaries to be generated automatically, and we show that the video summaries are displayed with a response time around one second.},
	doi = {10.1145/2557642.2579374},
	author = {Mortensen, Asgeir and Vamsidhar Reddy {Gaddam} and H{\r a}kon Kvale {Stensland} and Griwodz, Carsten and Johansen, Dag and Halvorsen, P{\r a}l}
}
@article {stensland-bagadus,
	title = {Bagadus: an Integrated Real-Time System for Soccer Analytics},
	journal = {ACM Transactions on Multimedia Computing, Communications, and Applications},
	volume = {10},
	number = {1s},
	year = {2014},
	month = {January},
	pages = {14:1-14:21},
	abstract = {The importance of winning has increased the role of performance analysis in the sports industry, and this underscores how statistics and technology keep changing the way sports are played. Thus, this is a growing area of interest, both from a computer system view in managing the technical challenges and from a sport performance view in aiding the development of athletes. In this respect, Bagadus is a real-time prototype of a sports analytics application using soccer as a case study. Bagadus integrates a sensor system, a soccer analytics annotations system, and a video processing system using a video camera array. A prototype is currently installed at Alfheim Stadium in Norway, and in this article, we describe how the system can be used in real-time to playback events. The system supports both stitched panorama video and camera switching modes and creates video summaries based on queries to the sensor system. Moreover, we evaluate the system from a systems point of view, benchmarking different approaches, algorithms, and trade-offs, and show how the system runs in real time.},
	doi = {10.1145/2541011},
	author = {H{\r a}kon Kvale {Stensland} and Vamsidhar Reddy {Gaddam} and Tenn{\o}e, Marius and Espen O. {Helgedagsrud} and N{\ae}ss, Mikkel and Henrik Kjus {Alstad} and Mortensen, Asgeir and Langseth, Ragnar and Lj{\o}dal, Sigurd and Landsverk, {\O}ystein and Griwodz, Carsten and Halvorsen, P{\r a}l and Stenhaug, Magnus and Johansen, Dag}
}
@inproceedings {mmsys.2014-02-12.5304482962,
	title = {Be Your Own Cameraman: Real-Time Support for Zooming and Panning Into Stored and Live Panoramic Video},
	journal = {Proceedings of the 5th annual ACM conference on Multimedia Systems (MMSYS)},
	year = {2014},
	month = {March},
	publisher = {ACM},
	type = {Conference},
	abstract = {High-resolution panoramic video with a wide field-of-view is popular in many contexts. However, in many examples, like surveillance and sports, it is often desirable to zoom and pan into the generated video. A challenge in this respect is real-time support, but in this demo, we present an end-to-end real-time panorama system with interactive zoom and panning. Our system installed at Alfheim stadium, a Norwegian premier league soccer team, generates a cylindrical panorama from five 2K cameras live where the perspective is corrected in real-time when presented to the client. This gives a better and more natural zoom compared to existing systems using perspective panoramas and zoom operations using plain crop. Our experimental results indicate that virtual views can be generated far below the frame-rate threshold, i.e., on a GPU, the processing requirement per frame is about 10\~milliseconds. The proposed demo lets participants interactively zoom and pan into stored panorama videos generated at Alfheim stadium and from a live 2-camera array on-site.},
	doi = {10.1145/2557642.2579370},
	author = {Vamsidhar Reddy {Gaddam} and Langseth, Ragnar and H{\r a}kon Kvale {Stensland} and Gurdjos, Pierre and Charvillat, Vincent and Griwodz, Carsten and Johansen, Dag and Halvorsen, P{\r a}l}
}
@article {stensland-ijsc,
	title = {Processing Panorama Video in Real-Time},
	journal = {International Journal of Semantic Computing},
	volume = {8},
	number = {2},
	year = {2014},
	month = {September},
	pages = {209-227},
	abstract = {There are many scenarios where high resolution, wide field of view video is useful. Such panorama video may be generated using camera arrays where the feeds from multiple cameras pointing at different parts of the captured area are stitched together. However, processing the different steps of a panorama video pipeline in real-time is challenging due to the high data rates and the stringent timeliness requirements. In our research, we use panorama video in a sport analysis system called Bagadus. This system is deployed at Alfheim stadium in Troms{\o}, and due to live usage, the video events must be generated in real-time. In this paper, we describe our real-time panorama system built using a low-cost CCD HD video camera array. We describe how we have implemented different components and evaluated alternatives. The performance results from experiments ran on commodity hardware with and without co-processors like graphics processing units (GPUs) show that the entire pipeline is able to run in real-time.},
	doi = {10.1142/S1793351X14400054},
	author = {H{\r a}kon Kvale {Stensland} and Vamsidhar Reddy {Gaddam} and Tenn{\o}e, Marius and Espen O. {Helgedagsrud} and N{\ae}ss, Mikkel and Henrik Kjus {Alstad} and Griwodz, Carsten and Halvorsen, P{\r a}l and Johansen, Dag}
}
@inproceedings {acmmm2014hdrshort,
	title = {Real-Time HDR Panorama Video},
	journal = {ACM International Conference on Multimedia},
	year = {2014},
	month = {November},
	type = {Conference},
	abstract = {The interest for wide field of view panorama video is increasing. In this respect, we have an application that uses an array of cameras that overlook a soccer stadium. The input of these cameras are stitched together to provide a panoramic view of the stadium. One of the challenges we face is that large parts of the field are obscured by shadows on sunny days. Such circumstances cause unsatisfying video quality. We have therefore implemented and evaluated multiple algorithms related to high dynamic range (HDR) video. The evaluation shows that a combination of several approaches gives the most useful results in our scenario.},
	keywords = {Conference},
	author = {Kellerer, Lorenz and Vamsidhar Reddy {Gaddam} and Langseth, Ragnar and H{\r a}kon Kvale {Stensland} and Griwodz, Carsten and Johansen, Dag and Halvorsen, P{\r a}l}
}
@inproceedings {mmsys.2014-02-18.0527842670,
	title = {Soccer Video and Player Position Dataset},
	journal = {Proceedings of the 5th annual ACM conference on Multimedia Systems (MMSYS)},
	year = {2014},
	publisher = {ACM},
	type = {Conference},
	abstract = {This paper presents a dataset of body-sensor traces and corresponding videos from several professional soccer games captured in late 2013 at the Alfheim Stadium in Troms{\o}, Norway.Player data, including field position, heading, and speed are sampled at 20 Hz using the highly accurate ZXY Sport Tracking system. Additional per-player statistics, like total distance covered and distance covered in different speed classes, are also included with a 1 Hz sampling rate. The provided videos are in high-definition and captured using two stationary camera arrays positioned at an elevated position above the tribune area close to the center of the field. The camera array is configured to cover the entire soccer field, and each camera can be used individually or as a stitched panorama video. This combination of body-sensor data and videos enables computer-vision algorithms for feature extraction, object tracking, background subtraction, and similar, to be tested against the ground truth contained in the sensor traces.},
	author = {Svein Arne {Pettersen} and Johansen, Dag and Johansen, H{\r a}vard and Berg-Johansen, Vegard and Vamsidhar Reddy {Gaddam} and Mortensen, Asgeir and Langseth, Ragnar and Griwodz, Carsten and H{\r a}kon Kvale {Stensland} and Halvorsen, P{\r a}l}
}
@inproceedings {Simula.simula.2050,
	title = {Analysis of a Real-World HTTP Segment Streaming Case},
	journal = {Proceedings of the European Interactive TV Conference (EuroITV 2013)},
	year = {2013},
	month = {June},
	pages = {75-84},
	publisher = {ACM},
	type = {Conference},
	abstract = {Today, adaptive HTTP segment streaming is a popular way to deliver video content to users. The benefits of HTTP segment streaming include its scalability, high performance and easy deployment, especially the possibility to reuse the already deployed HTTP infrastructure. However, current research focuses merely on client side statistics like for example achieved video qualities and adaption algorithms. To quantify the properties of such streaming systems from a service provider point of view, we have analyzed both sender and receiver side logging data provided by a popular Norwegian streaming provider. For example, we observe that more than 90\% of live streaming clients send their requests for the same video segment with an inter-arrival time of only 10 seconds. Moreover, the logs indicate that the server sends substantially less data than is actually reported to be received by the clients, and the origin server streams data to less clients than there really are. Based on these facts, we conclude that HTTP segment streaming really makes use of the HTTP cache infrastructure without the need to change anything in the parts of Internet that are not controlled by the streaming provider.},
	author = {Kupka, Tomas and Griwodz, Carsten and Halvorsen, P{\r a}l and Johansen, Dag and Hovden, Torgeir},
	editor = {George Lekakos Pa {Cremonesi}}
}
@inproceedings {mmsys.2013-03-15.9787518415,
	title = {Bagadus: an Integrated System for Arena Sports Analytics - a Soccer Case Study -},
	journal = {Proceedings of the 4th annual ACM conference on Multimedia Systems (MMSYS)},
	year = {2013},
	month = {March},
	pages = {48-59},
	publisher = {ACM},
	type = {Conference},
	abstract = {Sports analytics is a growing area of interest, both from a computer system view to manage the technical challenges and from a sport performance view to aid the development of athletes. In this paper, we present Bagadus, a prototype of a sports analytics application using soccer as a case study. Bagadus integrates a sensor system, a soccer analytics annotations system and a video processing system using a video camera array. A prototype is currently installed at Alfheim Stadium in Norway, and in this paper, we describe how the system can follow and zoom in on particular player(s). Next, the system will playout events from the games using stitched panorama video or camera switching mode and create video summaries based on queries to the sensor system. Further- more, we evaluate the system from a systems point of view, benchmarking different approaches, algorithms and trade-offs.},
	isbn = {978-1-4503-1894-5},
	author = {Halvorsen, P{\r a}l and S{\ae}grov, Simen and Mortensen, Asgeir and David Kai Christ {Kristensen} and Eichhorn, Alexander and Stenhaug, Magnus and Dahl, Stian and H{\r a}kon Kvale {Stensland} and Vamsidhar Reddy {Gaddam} and Griwodz, Carsten and Johansen, Dag},
	editor = {Zimmermann, Roger}
}
@inproceedings {Simula.simula.2150,
	title = {Combining Video and Player Telemetry for Evidence-Based Decisions in Soccer},
	journal = {Proceedings of the International Congress on Sports Science Research and Technology Support (icSPORTS)},
	year = {2013},
	month = {September},
	type = {Conference},
	abstract = {Technology is changing how soccer clubs train and interact with their supporters. Systems that provide acquisition and visualization of low-level player telemetry, like distance covered and speed, are already being widely adopted. A key observation is that such data when correlated with actual in-game video footage is a powerful tool for evidence-based decisions. As data volume and complexity grow, efficient tools for auto- mated high-precision retrieval become essential. This paper describes the unique combination of a radio-based sensor platform and several custom video retrieval systems in operational use at Troms{\o} Idrettslag (TIL), a Norwegian premier league soccer club. The systems have been developed using an experimental computer- science method where several prototypes were built and deployed for evaluation in close collaboration with the intended users. Although our method of computer-system prototyping has not yielded commercial quality products, it has enabled us to construct several novel systems combining low-level player telemetry with video retrieval, annotations, and user-centric security.},
	author = {Johansen, H{\r a}vard and Svein Arne {Pettersen} and Halvorsen, P{\r a}l and Johansen, Dag},
	editor = {Jo{\~a}o Barreiros {Correia}}
}
@inproceedings {ism.2013-11-29.3515257247,
	title = {Efficient Implementation and Processing of a Real-Time Panorama Video Pipeline},
	journal = {IEEE International Symposium on Multimedia (ISM 2013)},
	year = {2013},
	pages = {76-83},
	publisher = {IEEE},
	type = {Conference},
	abstract = {High resolution, wide field of view video generated from multiple camera feeds has many use cases. However, processing the different steps of a panorama video pipeline in real-time is challenging due to the high data rates and the stringent requirements of timeliness. We use panorama video in a sport analysis system where video events must be generated in real-time. In this respect, we present a system for real-time panorama video generation from an array of low-cost CCD HD video cameras. We describe how we have implemented different components and evaluated alternatives. We also present performance results with and without co- processors like graphics processing units (GPUs), and we evaluate each individual component and show how the entire pipeline is able to run in real-time on commodity hardware.},
	keywords = {Conference},
	author = {Tenn{\o}e, Marius and Espen O. {Helgedagsrud} and N{\ae}ss, Mikkel and Henrik Kjus {Alstad} and H{\r a}kon Kvale {Stensland} and Vamsidhar Reddy {Gaddam} and Johansen, Dag and Griwodz, Carsten and Halvorsen, P{\r a}l}
}
@article {Simula.simula.708,
	title = {Search-Based Composition, Streaming and Playback of Video Archive Content},
	journal = {Multimedia Tools and Applications},
	number = {online first},
	year = {2011},
	abstract = {Locating content in existing video archives is both a time and bandwidth consuming process since users might have to download and manually watch large portions of superfluous videos. In this paper, we present two novel prototypes using an Internet based video composition and streaming system with a keyword-based search interface that collects, converts, analyses, indexes, and ranks video content. At user requests, the system can automatically sequence out portions of single videos or aggregate content from multiple videos to produce a single, personalized video stream on-the-fly.},
	doi = {10.1007/ s11042-011-0847-5},
	author = {Johansen, Dag and Halvorsen, P{\r a}l and Johansen, H{\r a}vard and Riiser, Haakon and Gurrin, Cathal and Olstad, Bj{\o}rn and Griwodz, Carsten and Kvalnes, {\r A}ge and Hurley, Joseph and Kupka, Tomas}
}
@inproceedings {Simula.mpg.22,
	title = {Composing Personalized Video Playouts Using Search},
	journal = {Proceedings of the International Workshop on Visual Content Identification and Search (VCIDS) - ICME workshops},
	year = {2010},
	month = {July},
	pages = {1534-1539},
	publisher = {IEEE},
	type = {Workshop},
	abstract = {We conjecture that composition of video events from various sources into personalized video playouts will become an important part of next generation streaming systems. Here, video search is a key component since it enables users to retrieve candidate video events based on their interests. One of the main challenges, however, is to analyze the videos in order to correctly identify the various events used to annotate and index the video data. Key problems with current video analysis solutions include that they 1) are complex and therefore require a lot of processing time resulting in large delays; 2) that they can only identify a limited set of events; and 3) that they are still too inaccurate, both giving false positives and failing to find all events. In our DAVVI prototype, we therefore extract metadata for our video search engine by combining existing automatic video analysis tools with currently untapped textual information available in the Internet. This provides an end-user experience where textual query results can be combined dynamically into seamless, highly personalized video playouts using an adaptive torrent-like HTTP streaming solution.},
	isbn = {978-1-4244-7491-2},
	author = {Johansen, Dag and Johansen, H{\r a}vard and Halvorsen, P{\r a}l and Olstad, Bj{\o}rn and Gurrin, Cathal and Griwodz, Carsten},
	editor = {Lu, Jian and Hua, Xian-Sheng and China Dong {Xu}}
}
@inproceedings {Simula.mpg.20,
	title = {Low Overhead Container Format for Adaptive Streaming},
	journal = {Proceedings of the ACM Multimedia Systems (MMSys)},
	year = {2010},
	month = {February},
	pages = {193-198},
	publisher = {ACM},
	type = {Conference},
	abstract = {Current segmented HTTP streaming systems provide scal- able and quality adaptive video delivery services to a huge number of users. However, while they support a wide range of bandwidths and enable arbitrary content-based composition, their current formats have shortcomings like large overheads, live streaming delays, etc. We have therefore de- veloped an adaptive media player that works around these problems while still using standard components like H.264/AVC for video, and MP3 for audio. The system\&$\#$39;s adaptivity allows the player to pick a quality level that makes good use of available bandwidth and CPU resources while at the same time maintaining smooth uninterrupted playback, as well as offering near instant seek and startup times. This paper presents an appropriate way of coding the seg- ments and a simple multimedia container format that is optimized for adaptive streaming and video composition over HTTP. We show that our format is sufficiently advanced to contain any payload type, while being trivial to parse and translate to other container formats. Additionally, we show that our format is second to none in terms of overhead, without incurring any penalties on live streaming.},
	isbn = {978-1-60558-914-5},
	author = {Riiser, Haakon and Halvorsen, P{\r a}l and Griwodz, Carsten and Johansen, Dag},
	editor = {Mayer-Patel, Ketan and Feng, Wu-chi}
}
@article {Simula.mpg.30,
	title = {The Nornir Run-Time System for Parallel Programs Using Kahn Process Networks on Multi-Core Machines - a Flexible Alternative to MapReduce},
	journal = {The Journal of Supercomputing},
	number = {Online first},
	year = {2010},
	note = {In print: January 2013, Volume 63, Issue 1, pp 191-217},
	pages = {1-27},
	abstract = {Even though shared-memory concurrency is a paradigm frequently used for developing parallel applications on small- and middle-sized machines, experience has shown that it is hard to use. This is largely caused by synchro- nization primitives which are low-level, inherently nondeterministic, and, consequently, non-intuitive to use. In this paper, we present the Nornir run-time system. Nornir is comparable to well-known frameworks such as MapReduce and Dryad that are recognized for their efficiency and simplicity. Unlike these frameworks, Nornir also supports process structures containing branches and cycles. Nornir is based on the formalism of Kahn process networks, which is a shared-nothing, message-passing model of concurrency. We deem this model a simple and deterministic alternative to shared-memory concurrency. Experiments with real and synthetic benchmarks on up to 8 CPUs show that per- formance in most cases scales almost linearly with the number of CPUs, when not limited by data dependencies. We also show that the modeling flexibility allows Nornir to outperform its MapReduce counter-parts using well-known benchmarks.},
	doi = {10.1007/s11227-010-0503-2},
	author = {Vrba, Zeljko and Halvorsen, P{\r a}l and Griwodz, Carsten and Beskow, Paul and Espeland, H{\r a}vard and Johansen, Dag}
}
@article {Simula.mpg.24,
	title = {Support for Enterprise Consolidation of I/O Bound Services},
	journal = {Software: Practice and Experience},
	volume = {40},
	number = {10},
	year = {2010},
	pages = {1035-1051},
	abstract = {In this article, we evaluate the performance effects of I/O bound workloads on a specific virtual machine, an important component of an enterprise cloud computing infrastructure. In particular, we demonstrate that (1) the I/O workload of one guest system may adversely affect the I/O performance of another for the XEN hypervisor and (2) the general I/O performance is degraded due to various overheads. Next, we have devised a light-weight, complementary, backwards-compatible alternative to hypervisor-based virtualization techniques called Bonsai. Our software provides low-overhead I/O performance isolation by transparently applying traffic shaping to system calls in a cost-effective manner. Using this system, I/O resource consumption can be controlled at a very fine granularity. Furthermore, using video streaming experiments, where we limit the I/O bandwidth available to each service, we show that we are able to achieve the required level of resource isolation on a per process basis with only a negligible CPU overhead and without reducing the I/O performance.},
	author = {Kvalnes, {\r A}ge and Johansen, Dag and Griwodz, Carsten and Halvorsen, P{\r a}l}
}
@inproceedings {Simula.mpg.34,
	title = {VESP: a Video-Enabled Enterprise Search Platform},
	journal = {Proceeding of the International Conference on Data and Knowledge Engineering (ICDKE)},
	year = {2010},
	publisher = {IEEE Computer Society},
	type = {Conference},
	abstract = {In this paper, we present how to provide a novel and potentially disruptive multimedia service by modifying a widely deployed commercial enterprise search engine. The idea is to transparently integrate rich multimedia data with traditional textual-oriented query results. This includes that the search engine automatically discovers and extracts relevant scenes from a large knowledge repository of existing videos and produces a new, customized video of events matching the user query. To evaluate our prototype, we have performed experiments using a data set from a knowledge repository in Microsoft consisting of PowerPoint presentations with corresponding videos. Our initial results demonstrate that such integration can be implemented efficiently, and that potential users prefer to have the opportunity to enrich the search results with corresponding video.},
	author = {Halvorsen, P{\r a}l and Johansen, Dag and Olstad, Bj{\o}rn and Kupka, Tomas and Tenn{\o}e, Sverre},
	editor = {Xiang, Yang and Samarati, Pierangela and Hu, Jiankun and Zhou, Wanlei and Sadeghi, Ahmad-Reza}
}
@inproceedings {Simula.mpg.26,
	title = {VESP: Enriching Enterprise Document Search Results With Aligned Video Summarization},
	journal = {Proceedings of the International Conference on Multimedia (ACM MM)},
	year = {2010},
	month = {October},
	pages = {1603-1604},
	publisher = {ACM},
	type = {Conference},
	abstract = {In this demo, we present a video-enabled enterprise search platform (vESP), an application prototype that enhances a widely deployed commercial enterprise search engine with video streaming. In large enterprises, there exists a lot of information in form of presentations with corresponding video. Using our enhancements, a user can select and combine slides from different presentations generating a new slide deck dynamically and the corresponding video clips are concatenated and presented vis-a-vis the slides on-the-fly. The prototype is evaluated using a data set from Microsoft, and our initial user surveys indicate that the opportunity to enrich the search results with corresponding video is embraced by potential users.},
	isbn = {978-1-60558-933-6},
	author = {Halvorsen, P{\r a}l and Johansen, Dag and Olstad, Bj{\o}rn and Kupka, Tomas and Tenn{\o}e, Sverre},
	editor = {Shih-Fu Chang Albe {del Bimbo}}
}
@inproceedings {Simula.ND.391,
	title = {DAVVI: a Prototype for the Next Generation Multimedia Entertainment Platform},
	journal = {ACM Multimedia Conference},
	year = {2009},
	note = {Demo paper.},
	month = {October},
	pages = {989-990},
	publisher = {ACM},
	type = {Conference},
	abstract = {In this demo, we present DAVVI, a prototype of the next generation multimedia entertainment platform. It delivers multi-quality video content in a torrent-similar way like known systems from Move Networks, Microsoft and Apple do. However, it also provides a brand new, personalized user experience. Through applied search, personalization and recommendation technologies, end-users can efficiently search and retrieve highlights and combine arbitrary events in a customized manner using drag and drop. The created playlists of video segments are then delivered back to the system to improve future search and recommendation results. Here, we demonstrate this system using \mbox{a soccer example.}},
	isbn = {978-1-60558-608-3},
	author = {Johansen, Dag and Johansen, H{\r a}vard and Aarflot, Tjalve and Hurley, Joseph and Kvalnes, {\r A}ge and Gurrin, Cathal and Sav, Sorin and Olstad, Bj{\o}rn and Aaberg, Erik and Endestad, Tore and Riiser, Haakon and Griwodz, Carsten and Halvorsen, P{\r a}l},
	editor = {Gao, Wen and Rui, Yong and Hanjalic, Alan and Xu, Changsheng and Steinbach, Eckehard and Abdulmotaleb El {Saddik} and Zhou, Michelle}
}
@inproceedings {Simula.ND.397,
	title = {The Nornir Run-Time System for Parallel Programs Using Kahn Process Networks},
	journal = {Proceedings of 6th International Conference on Network and Parallel Computing (NPC)},
	year = {2009},
	pages = {1-8},
	publisher = {IEEE Computer Society},
	type = {Conference},
	abstract = {Shared-memory concurrency is the prevalent paradigm used for developing parallel applications targeted towards small- and middle-sized machines, but experience has shown that it is hard to use. This is largely caused by synchronization primitives which are low-level, inherently nondeterministic, and, consequently, non-intuitive to use. In this paper, we present the \textit{Nornir} run-time system. Nornir is comparable to well-known frameworks like MapReduce and Dryad, but has additional support for process structures containing cycles. It is based on the formalism of Kahn process networks, which we deem as a simple and deterministic alternative to shared-memory concurrency. Experiments with real and synthetic benchmarks on up to 8 CPUs show that performance in most cases improves almost linearly with the number of CPUs, when not limited by data dependencies.},
	isbn = {978-0-7695-3837-2},
	author = {Vrba, Zeljko and Halvorsen, P{\r a}l and Griwodz, Carsten and Beskow, Paul and Johansen, Dag}
}
