@article {25860,
	title = {Top-Down Saliency Detection Driven by Visual Classification},
	journal = {Computer Vision and Image Understanding},
	volume = {172},
	year = {2018},
	pages = {67-76},
	publisher = {Elsevier},
	abstract = {This paper presents an approach for saliency detection able to emulate the integration of the top-down (task-controlled) and bottom-up (sensory information) processes involved in human visual attention. In particular, we first learn how to generate saliency when a specific visual task has to be accomplished. Afterwards, we investigate if and to what extentthe learned saliency maps can support visual classification in nontrivial cases. To achieve this, we propose SalClass-Net, a CNN framework consisting of two networks jointly trained: a) the first one computing top-down saliency mapsfrom input images, and b) the second one exploiting the computed saliency maps for visual classification.To test our approach, we collected a dataset of eye-gaze maps, using a Tobii T60 eye tracker, by asking several subjectsto look at images from the Stanford Dogs dataset, with the objective of distinguishing dog breeds.Performance analysis on our dataset and other saliency benchmarking datasets, such as POET, showed that Sal-ClassNet outperforms state-of-the-art saliency detectors, such as SalNet and SALICON. Finally, we also analyzedthe performance of SalClassNet in a fine-grained recognition task and found out that it yields enhanced classificationaccuracy compared to Inception and VGG-19 classifiers. The achieved results, thus, demonstrate that 1) condition-ing saliency detectors with object classes reaches state-of-the-art performance, and 2) explicitly providing top-downsaliency maps to visual classifiers enhances accuracy.},
	doi = {10.1016/j.cviu.2018.03.005},
	author = {Murabito, Francesca and Spampinato, Concetto and Palazzo, Simone and Giordano, Daniela and Pogorelov, Konstantin and Riegler, Michael}
}
@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}
}
@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 {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 {24857,
	title = {The MediaEval 2016 Context of Experience Task: Recommending Videos Suiting a Watching Situation},
	journal = {MediaEval 2016 Workshop},
	year = {2016},
	month = {10/2016},
	publisher = {CEUR Workshop Proceedings},
	address = {Hilversum, Netherlands},
	abstract = {In this paper we present an overview of the Context of Experience Task: recommending videos suiting a watching situation which is part of the MediaEval 2016 Benchmark. The aim of the task is to explore multimedia content that is watched under a certain situation. The scope of the this years task lies on movies watched during a flight. We hypothesize that users will have different preferences for movies that are watched during a flight compared to when a movie is watched at home or the cinema. This is most probably influenced by the context and the devices used to watch. In the case of being on a flight, the context is clearly different to normal situation (noise, compact, bad air) and also the devices differ (small screens, bad audio quality). The main goal of the task is to estimate if a person would like to watch a certain movie on the airplane or not. As dataset we provide a large collection of movies, collected from an airline, including pre-extracted visual, text and audio features.},
	author = {Riegler, Michael and Spampinato, Concetto and Griwodz, Carsten and Halvorsen, P{\r a}l}
}
@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}
}
@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 {23569,
	title = {Introduction to a Task on Context of Experience: Recommending Videos Suiting a Watching Situation},
	journal = {MediaEval Benchmarking Initiative for Multimedia Evaluation},
	year = {2015},
	month = {09/2015},
	publisher = {CEUR Workshop Proceedings},
	address = {Wurzen, Germany},
	author = {Riegler, Michael and Spampinato, Concetto and Markussen, Jonas and Halvorsen, P{\r a}l and Griwodz, Carsten}
}
