Multiple feature hashing for real-time large scale near-duplicate video retrieval
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Scene signatures for unconstrained news video stories
MMM'12 Proceedings of the 18th international conference on Advances in Multimedia Modeling
Near-duplicate video retrieval: Current research and future trends
ACM Computing Surveys (CSUR)
ACM Transactions on Information Systems (TOIS)
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Online video content is surging to an unprecedented level. Massive video publishing and sharing impose heavy demands on online near-duplicate detection for many novel video applications. This paper presents an accurate and practical system for online near-duplicate subsequence detection over continuous video streams. We propose to transform a video stream into a one-dimensional video distance trajectory (VDT) monitoring the continuous changes of consecutive frames with respect to a reference point, which is further segmented and represented by a sequence of compact signatures called linear smoothing functions (LSFs). LSFs of each subsequence of the incoming video stream are continuously generated and temporally stored in a buffer for comparison with query LSFs. LSF adopts compound probability to combine three independent video factors for effective segment similarity measure, which is then utilized to compute sequence similarity for near-duplicate detection. To avoid unnecessary sequence similarity computations, an efficient sequence skipping strategy is also embedded. Experimental results on detecting diverse near-duplicates of TV commercials in real video streams show the superior performance of our system on both effectiveness and efficiency over existing methods.