The MediaMill at TRECVID 2013: : Searching concepts, Objects, Instances and events in video

Cees G. M. Snoek, Koen E. A. van de Sande, Daniel Fontijne, AmirHossein Habibian, Mihir Jain, Svetlana Kordumova, Zhenyang Li, Masoud Mazloom, Silvia L. Pintea, Ran Tao, Dennis C. Koelma, Arnold W. M. Smeulders
2013 TREC Video Retrieval Evaluation  
In this paper we summarize our TRECVID 2013 [15] video retrieval experiments. The MediaMill team participated in four tasks: concept detection, object localization, instance search, and event recognition. For all tasks the starting point is our top-performing bag-of-words system of TRECVID 2008-2012, which uses color SIFT descriptors, average and difference coded into codebooks with spatial pyramids and kernel-based machine learning. New this year are concept detection with deep learning,
more » ... t detection without annotations, object localization using selective search, instance search by reranking, and event recognition based on concept vocabularies. Our experiments focus on establishing the video retrieval value of the innovations. The 2013 edition of the TRECVID benchmark has again been a fruitful participation for the MediaMill team, resulting in the best result for concept detection, concept detection without annotation, object localization, concept pair detection, and visual event recognition with few examples.
dblp:conf/trecvid/SnoekSFHJKLMP0K13 fatcat:5jqwbazglzad5fxibp2o7423x4