@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}
}
@inproceedings {27331,
	title = {Toadstool: A Dataset for Training Emotional IntelligentMachines Playing Super Mario Bros},
	journal = {The ACM Multimedia Systems Conference (MMSys)},
	year = {2020},
	publisher = {ACM},
	address = {The ACM Multimedia Systems Conference (MMSys)},
	abstract = {Games are often defined as engines of experience, and they are heavily relying on emotions, they arouse in players. In this paper, we present a dataset called Toadstool\&nbsp;as well as a reproducible methodology to extend on the dataset. The dataset consists of video, sensor, and demographic data collected from ten participants playing Super Mario Bros, an iconic and famous video game. The sensor data is collected through an Empatica E4 wristband, which provides high-quality measurements and is graded as a medical device. In addition to the dataset and the methodology for data collection, we present a set of baseline experiments which show that we can use video game frames together with the facial expressions to predict the blood volume pulse of the person playing Super Mario Bros. With the dataset and the collection methodology we aim to contribute to research on emotionally aware machine learning algorithms, focusing on reinforcement learning and multimodal data fusion. We believe that the presented dataset can be interesting for a manifold of researchers to explore exciting new interdisciplinary questions.\&nbsp;},
	doi = {10.1145/3339825.3394939},
	url = {https://dl.acm.org/doi/10.1145/3339825.3394939},
	author = {Svoren, Henrik and Thambawita, Vajira and Halvorsen, P{\r a}l and Jakobsen, Petter and Garcia-Ceja, Enrique and Farzan Majeed {Noori} and Hugo Lewi {Hammer} and Lux, Mathias and Riegler, Michael and Hicks, Steven}
}
@inproceedings {27106,
	title = {GameStory Task at MediaEval 2019},
	journal = {Proceedings of MediaEval 2019},
	year = {2019},
	month = {10/2019},
	publisher = {CEUR Workshop Proceedings (CEUR-WS.org)},
	abstract = {Game video streams are watched by millions, so that, meanwhile, one can make a living from broadcasting and commenting video games, whereas some have become professional e-sports athletes. E-sports leagues and tournaments have emerged worldwide, where players compete in controlled environments, streaming the matches online, and allowing the audience to discuss and criticize the game- play. In the GameStory task, held for the second time at MediaEval, we foster research into this exciting domain. Our focus is on an- alyzing and summarizing video game streams. With the help of\&nbsp;ZNIPE.tv, we compiled a high-quality dataset of a\&nbsp;Counter-Strike: Global Offensive\&nbsp;tournament alongside ground truth labels for two analysis tasks, forming a basis for summarization.},
	author = {Lux, Mathias and Riegler, Michael and Dang-Nguyen, Duc-Tien and Pirker, Johanna and Potthast, Martin and Halvorsen, P{\r a}l}
}
@inproceedings {26931,
	title = {Medico Multimedia Task at MediaEval 2019},
	journal = {MediaEval},
	year = {2019},
	publisher = {CEUR Workshop Proceedings},
	abstract = {The Medico: Multimedia for Medicine Task\&nbsp;is running for the third time as part of MediaEval 2019. This year, we have changed the task from anomaly detection in images of the gastrointestinal tract to focus on the automatic prediction of human semen quality based on videos. The purpose of this task is to aid in the assessment of male reproductive health by providing a quick and consisted method of analyzing human semen. In this paper, we describe the task in detail, give a brief description of the provided dataset, and discuss the evaluation process and the metrics used to rank the submissions of the participants.},
	author = {Hicks, Steven and Halvorsen, P{\r a}l and Trine B. {Haugen} and Jorunn M. {Andersen} and Witczak, Oliwia and Pogorelov, Konstantin and Hugo Lewi {Hammer} and Dang-Nguyen, Duc-Tien and Lux, Mathias and Riegler, Michael}
}
@inproceedings {26929,
	title = {Predicting Sperm Motility and Morphology using Deep Learning and Handcrafted Features},
	journal = {MediaEval},
	year = {2019},
	publisher = {ceur ws org},
	abstract = {This paper presents the approach proposed by the organizer team (SimulaMet) for MediaEval 2019 Multimedia for Medicine: The Medico Task. The approach uses a data preparation method which is based on global features extracted from multiple frames within each video and then combines this with information about the patient in order to create a compressed representation of each video. The goal is to create a less hardware expensive data representation that still retains the temporal information of the video and related patient data. Overall, the results need some improvement before being a viable option for clinical use.},
	author = {Hicks, Steven and Halvorsen, P{\r a}l and Trine B. {Haugen} and Jorunn M. {Andersen} and Witczak, Oliwia and Pogorelov, Konstantin and Hugo Lewi {Hammer} and Dang-Nguyen, Duc-Tien and Lux, Mathias and Riegler, Michael}
}
@inproceedings {26587,
	title = {Summarizing E-Sports Matches and Tournaments: The Example of Counter-Strike: Global Offensive},
	journal = {International Workshop on Immersive Mixed and Virtual Environment Systems (MMVE)},
	year = {2019},
	publisher = {ACM},
	abstract = {That video and computer games have reached the masses is a well known fact. Furthermore, game streaming and watching other people play video games is another phenomenon that has outgrown its small beginning by far, and game streams, be it live or recorded, are today viewed by millions. E-sports is the result of organized leagues and tournaments in which players can compete in controlled environments and viewers can experience the matches, discuss and criticize, just like in physical sports. \&nbsp;However, as traditional sports, e-sports matches may be long and contain less interesting parts, introducing the challenge of producing well directed summaries and highlights.\&nbsp;In this paper, we describe our efforts to approach the game streaming and e-sports phenomena from a multimedia research point of view. We focus on the challenge of summarizing matches from specific relevant game, Counter-Strike: Global Offensive (CS:GO). We survey related work, describe the rules and structure of the game and identify the main challenges for summarizing e-sports matches. With this contribution, we aim to foster multimedia research in the area of e-sports and game streaming.},
	author = {Lux, Mathias and Halvorsen, P{\r a}l and Dang-Nguyen, Duc-Tien and H{\r a}kon Kvale {Stensland} and Kesavulu, Manoj and Potthast, Martin and Riegler, Michael}
}
@inproceedings {26273,
	title = {The 2018 Medico Multimedia Task Submission of Team NOAT using Neural Network Features and Search-based Classification},
	journal = {Proceeding of the MediaEval Benchmarking Initiative for Multimedia Evaluation},
	year = {2018},
	month = {10/2018},
	publisher = {CEUR Workshop Proceedings},
	abstract = {In this paper, we describe our approach for the classification of medical images depicting the human gastrointestinal tract. Search-based classification is performed in three stages. In the first stage, we extract deep features for each image using pre-trained deep-learning models. In the second stage, we use LIRE \&nbsp;to index the generated features, so that we can then, in the final stage, search the index for similar images and make our predictions based on the results. With this approach, we achieved a MCC score of 0,54\&nbsp;and a accuracy of 0,94, which shows that deep features combined with search-based classification are a viable option for medical image analysis.\&nbsp;\&nbsp;},
	author = {Steiner, Michael and Lux, Mathias and Halvorsen, P{\r a}l}
}
@inproceedings {25950,
	title = {Comprehensible Reasoning and Automated Reporting of Medical Examinations Based on Deep Learning Analysis},
	journal = {Proceedings of the 9th ACM Multimedia Systems Conference},
	year = {2018},
	pages = {490-493},
	publisher = {ACM},
	address = {Amsterdam, Netherlands},
	isbn = {978-1-4503-5192-8},
	doi = {10.1145/3204949.3208113},
	author = {Hicks, Steven and Pogorelov, Konstantin and Lux, Mathias and Jeppsson, Mattis and Kristin Ranheim {Randel} and de Lange, Thomas and Sigrun Losadal {Eskeland} and Halvorsen, P{\r a}l and Riegler, Michael}
}
@inproceedings {26274,
	title = {GameStory Task at MediaEval 2018},
	journal = {Proceeding of the MediaEval Benchmarking Initiative for Multimedia Evaluation},
	year = {2018},
	month = {10/2018},
	publisher = {CEUR Workshop Proceedings},
	abstract = {That video games have reached the masses is well known. Moreover, game streaming and watching other people play video games is a phenomenon that has outgrown its small beginnings. Game video streams, be it live or recorded, are viewed by millions. E-sports is the result of organized leagues and tournaments in which players can compete in controlled environments and viewers can experience the matches, discuss and criticize, just like in physical sports. In the GameStory task, taking place the first time in 2018, we approach the game streaming and e-sports phenomena from a multimedia research side. We focus on the task of summarizing matches using a specific relevant game,\&nbsp;Counter-Strike: Global Offensive, as a case study. With the help of ZNIPE.tv, we provide a data set of high quality data and meta data from competitive tournaments and aim to foster research in the area of e-sports and game streaming.},
	author = {Lux, Mathias and Riegler, Michael and Dang-Nguyen, Duc-Tien and Larson, Marcus and Potthast, Martin and Halvorsen, P{\r a}l}
}
@inproceedings {26256,
	title = {Medico Multimedia Task at MediaEval 2018},
	journal = {Working Notes Proceedings of the MediaEval 2018 Workshop},
	year = {2018},
	publisher = {CEUR Workshop Proceedings},
	abstract = {The Medico: Multimedia for Medicine Task, running for the second time as part of MediaEval 2018, focuses on detecting abnormalities, diseases, anatomical landmarks and other findings in images captured by medical devices in the gastrointestinal tract. The task is described, including the use case and its challenges, the dataset with ground truth, the required participant runs and the evaluation metrics.},
	author = {Pogorelov, Konstantin and Riegler, Michael and Halvorsen, P{\r a}l and Hicks, Steven and Kristin Ranheim {Randel} and Dang-Nguyen, Duc-Tien and Lux, Mathias and Ostroukhova, Olga and de Lange, Thomas}
}
@inproceedings {25953,
	title = {Mimir: An Automatic Reporting and Reasoning System for Deep Learning based Analysis in the Medical Domain},
	journal = {Proceedings of the 9th ACM Multimedia Systems Conference},
	year = {2018},
	pages = {369-374},
	publisher = {ACM},
	address = {Amsterdam, Netherlands},
	isbn = {978-1-4503-5192-8},
	doi = {10.1145/3204949.3208129},
	author = {Hicks, Steven and Lux, Mathias and de Lange, Thomas and Kristin Ranheim {Randel} and Jeppsson, Mattis and Pogorelov, Konstantin and Halvorsen, P{\r a}l and Riegler, Michael}
}
@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}
}
@inproceedings {26168,
	title = {Overview of ImageCLEF 2018: Challenges, datasets and evaluation},
	journal = {ImageCLEF 2018},
	year = {2018},
	month = {08/2018},
	publisher = { Springer},
	abstract = {This paper presents an overview of the ImageCLEF 2018 evaluation campaign, an event that was organized as part of the CLEF (Conference and Labs of the Evaluation Forum) Labs 2018. ImageCLEF is an ongoing initiative (it started in 2003) that promotes the evaluation of technologies for annotation, indexing and retrieval with the aim of providing information access to collections of images in various usage scenarios and domains. In 2018, the 16th edition of ImageCLEF ran three main tasks and a pilot task: (1) a\&nbsp;caption prediction\&nbsp;task that aims at predicting the caption of a figure from the biomedical literature based only on the figure image; (2) a\&nbsp;tuberculosis\&nbsp;task that aims at detecting the tuberculosis type, severity and drug resistance from CT (Computed Tomography) volumes of the lung; (3) a\&nbsp;LifeLog\&nbsp;task (videos, images and other sources) about daily activities understanding and moment retrieval, and (4) a pilot task on\&nbsp;visual question answering\&nbsp;where systems are tasked with answering medical questions. The strong participation, with over 100 research groups registering and 31 submitting results for the tasks, shows an increasing interest in this benchmarking campaign.},
	url = {https://link.springer.com/chapter/10.1007/978-3-319-98932-7_28},
	author = {Ionescu, Bogdan and M{\"u}ller, Henning and Villegas, Mauricio and Alba Garc{\'\i}a Se {de Herrera} and Eickhoff, Carsten and Andrearczyk, Vincent and Yashin Dicente {Cid} and Liauchuk, Vitali and Kovalev, Vassili and Sadid A {Hasan} and Ling, Yan and Farri, Oladimeji and Liu, Joey and Lungren, Matthew and Dang-Nguyen, Duc-Tien and Prias, Luca and Riegler, Michael and Zhou, Liting and Lux, Mathias and Gurrin, Cathal},
	editor = {Bellot, Patrice}
}
@inproceedings {25628,
	title = {A comparison of deep learning with global features for gastrointestinal disease detection},
	journal = {MediaEval Benchmark 2017},
	year = {2017},
	month = {09/2017},
	publisher = {CEUR-WS.org},
	address = {Dublin, Ireland},
	author = {Pogorelov, Konstantin and Riegler, Michael and Halvorsen, P{\r a}l and Griwodz, Carsten and de Lange, Thomas and Kristin Ranheim {Randel} and Sigrun Losada {Eskeland} and Dang-Nguyen, Duc-Tien and Olga, Ostroukhova and Lux, Mathias and Spampinato, Concetto},
	editor = {Riegler, Michael}
}
@misc {25630,
	title = {Interview column: introduction},
	year = {2017},
	month = {07/2017},
	publisher = {ACM},
	author = {Riegler, Michael and Engelbrecht, Herman and Lux, Mathias}
}
@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 {25267,
	title = {LireSolr - A Visual Information Retrieval Server},
	journal = {ACM International Conference on Multimedia Retrieval},
	year = {2017},
	month = {06/2017},
	publisher = {ACM},
	address = {Bucharest},
	isbn = {978-1-4503-4701-3},
	doi = {10.1145/3078971.3079014},
	author = {Lux, Mathias and Riegler, Michael and Halvorsen, P{\r a}l and MacStravic, Glenn},
	editor = {Ionescu, Bogdan and Sebe, Nicu}
}
@inproceedings {25627,
	title = {Multimedia for medicine: the medico Task at mediaEval 2017},
	journal = {MediaEval Benchmark 2017},
	year = {2017},
	month = {09/2017},
	publisher = {CEUR-WS.org},
	address = {Dublin, Ireland},
	author = {Riegler, Michael and Pogorelov, Konstantin and Halvorsen, P{\r a}l and Griwodz, Carsten and de Lange, Thomas and Kristin Ranheim {Randel} and Sigrun Losada {Eskeland} and Dang-Nguyen, Duc-Tien and Lux, Mathias and Spampinato, Concetto},
	editor = {Riegler, Michael}
}
@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 {24481,
	title = {Explorative Hyperbolic-Tree-Based Clustering Tool for Unsupervised Knowledge Discovery},
	journal = {International Workshop on Content-based Multimedia Indexing},
	year = {2016},
	month = {06/2016},
	publisher = {IEEE / ACM},
	author = {Riegler, Michael and Pogorelov, Konstantin and Lux, Mathias and Halvorsen, P{\r a}l and Griwodz, Carsten and Sigrun L. {Eskeland} and de Lange, Thomas}
}
@inproceedings {24475,
	title = {LIRE - Open Source Visual Information Retrieval},
	journal = {Multimedia System Conference 2016},
	year = {2016},
	month = {05/2016},
	publisher = {ACM},
	address = {New York},
	author = {Lux, Mathias and Riegler, Michael and Pogorelov, Konstantin and Halvorsen, P{\r a}l and Anagnostopoulos, Nektarios}
}
@inproceedings {24473,
	title = {Right inflight? A dataset for exploring the automatic prediction of movies suitable for a watching situation},
	journal = {Multimedia Systems Conference 2016},
	year = {2016},
	month = {05/2016},
	publisher = {ACM},
	address = {New York},
	author = {Riegler, Michael and Spampinato, Concetto and Halvorsen, P{\r a}l and Lux, Mathias and Markussen, Jonas and Pogorelov, Konstantin and Griwodz, Carsten and H{\r a}kon Kvale {Stensland}}
}
@inproceedings {2660509,
	title = {Event Understanding in Endoscopic Surgery Videos},
	journal = {HuEvent {\textquoteright}14: Proceedings of the 1st ACM International Workshop on Human Centered Event Understanding from Multimedia},
	year = {2014},
	pages = {17{\textendash}22},
	publisher = {ACM},
	address = {New York, NY, USA},
	isbn = {978-1-4503-3120-3},
	doi = {http://doi.acm.org/10.1145/2660505.2660509},
	author = {Guggenberger, Mario and Riegler, Michael and Lux, Mathias and Halvorsen, P{\r a}l}
}
@inproceedings {2655619,
	title = {Gone: An Interactive Experience for Two People},
	journal = {MM {\textquoteright}14: Proceedings of the ACM International Conference on Multimedia},
	year = {2014},
	pages = {701{\textendash}704},
	publisher = {ACM},
	address = {New York, NY, USA},
	isbn = {978-1-4503-3063-3},
	doi = {http://doi.acm.org/10.1145/2647868.2655619},
	author = {Riegler, Michael and Lux, Mathias and Zellot, Christian and Knoch, Lukas and Schnattler, Horst and Napetschnig, Sabrina and Kogler, Julian and Degendorfer, Claus and Spot, Norbert and Zoderer, Manuel}
}
@inproceedings {2654894,
	title = {How {\textquoteright}How{\textquoteright} Reflects What{\textquoteright}s What: Content-based Exploitation of How Users Frame Social Images},
	journal = {MM {\textquoteright}14: Proceedings of the ACM International Conference on Multimedia},
	year = {2014},
	pages = {397{\textendash}406},
	publisher = {ACM},
	address = {New York, NY, USA},
	isbn = {978-1-4503-3063-3},
	doi = {http://doi.acm.org/10.1145/2647868.2654894},
	author = {Riegler, Michael and Lux, Mathias and Kofler, Christoph}
}
@inproceedings {19163,
	title = {Mobile Picture Guess: A Crowdsourced Serious Game for Simulating Human Perception},
	journal = {Proceedings of the International Workshop on Social Media for Crowdsourcing and Human Computation},
	year = {2014},
	month = {10/2014},
	publisher = {Springer},
	abstract = {In this paper we present a novel idea that combines a mobilegame with a Crowdsourcing campaign. The game is designed for studiesinto the visual saliency of image segments, where the game objective isfor players to guess what is depicted in an image that is gradually uncovering.Game scores depend on the number of correct answers and thespeed at which these are provided. With these game mechanics, we candetermine the image segments that are most essential to players whenasked to guess the image content, thereby assessing the most salientimage regions. Through the combination of this game scenario and aCrowdsourcing campaign, we also present a way to tackle the rising demandsfor higher salaries in this line of work. By providing workers withan entertaining task, we aim to increase player motivation and hopefullymake them want to play longer than required. In this paper we alsopresent a sample study that evaluates the visual saliency of 200 animalimages from Flickr. We conclude with preliminary results from the studyand with our insights on how this approach can be applied},
	author = {Riegler, Michael and Eg, Ragnhild and Lux, Mathias and Schicho, Markus}
}
@inproceedings {2594789,
	title = {PictureSort: Gamification of Image Ranking},
	journal = {GamifIR {\textquoteright}14: Proceedings of the First International Workshop on Gamification for Information Retrieval},
	year = {2014},
	pages = {57{\textendash}60},
	publisher = {ACM},
	address = {New York, NY, USA},
	isbn = {978-1-4503-2892-0},
	doi = {http://doi.acm.org/10.1145/2594776.2594789},
	author = {Lux, Mathias and Guggenberger, Mario and Riegler, Michael}
}
@inproceedings {2582621,
	title = {VideoJot: A Multifunctional Video Annotation Tool},
	journal = {ICMR {\textquoteright}14: Proceedings of International Conference on Multimedia Retrieval},
	year = {2014},
	pages = {534{\textendash}537},
	publisher = {ACM},
	address = {New York, NY, USA},
	isbn = {978-1-4503-2782-4},
	doi = {http://doi.acm.org/10.1145/2578726.2582621},
	author = {Riegler, Michael and Lux, Mathias and Charvillat, Vincent and Carlier, Axel and Vliegendhart, Raynor}
}
@inproceedings {2483996,
	title = {Annotation of endoscopic videos on mobile devices: a bottom-up approach},
	journal = {MMSys {\textquoteright}13: Proceedings of the 4th ACM Multimedia Systems Conference},
	year = {2013},
	pages = {141{\textendash}145},
	publisher = {ACM},
	address = {New York, NY, USA},
	isbn = {978-1-4503-1894-5},
	doi = {http://doi.acm.org/10.1145/2483977.2483996},
	author = {Lux, Mathias and Riegler, Michael}
}
@inproceedings {riegler13,
	title = {Frame the Crowd: Global Visual Features Labeling boosted with Crowdsourcing Information},
	journal = {MediaEval 2013 Workshop, Barcelona, Spain},
	year = {2013},
	month = {10/2014},
	address = {CEUR-WS.org},
	author = {Riegler, Michael and Lux, Mathias and Kofler, Christoph}
}
