Multiple feature hashing for real-time large scale near-duplicate video retrieval
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Submodular video hashing: a unified framework towards video pooling and indexing
Proceedings of the 20th ACM international conference on Multimedia
Near-duplicate video retrieval: Current research and future trends
ACM Computing Surveys (CSUR)
Computer Vision and Image Understanding
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In this paper, we present a large database of over 50,000 user-labeled videos collected from YouTube. We develop a compact representation called “tiny videos” that achieves high video compression rates while retaining the overall visual appearance of the video as it varies over time. We show that frame sampling using affinity propagation—an exemplar-based clustering algorithm—achieves the best trade-off between compression and video recall. We use this large collection of user-labeled videos in conjunction with simple data mining techniques to perform related video retrieval, as well as classification of images and video frames. The classification results achieved by tiny videos are compared with the tiny images framework [24] for a variety of recognition tasks. The tiny images data set consists of 80 million images collected from the Internet. These are the largest labeled research data sets of videos and images available to date. We show that tiny videos are better suited for classifying scenery and sports activities, while tiny images perform better at recognizing objects. Furthermore, we demonstrate that combining the tiny images and tiny videos data sets improves classification precision in a wider range of categories.