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Video Copy-Detection and Localization with a Scalable Cascading Framework
July-Sept. 2013 (vol. 20 no. 3)
pp. 72-86
Yonghong Tian, Peking University
Tiejun Huang, Peking University
Menglin Jiang, Peking University
Wen Gao, Peking University
For video copy detection, no single audio-visual feature, or single detector based on several features, can work well for all transformations. This article proposes a novel video copy-detection and localization approach with scalable cascading of complementary detectors and multiscale sequence matching. In this cascade framework, a soft-threshold learning algorithm is utilized to estimate the optimal decision thresholds for detectors, and a multiscale sequence matching method is employed to precisely locate copies using a 2D Hough transform and multigranularities similarity evaluation. Excellent performance on the TRECVID-CBCD 2011 benchmark dataset shows the effectiveness and efficiency of the proposed approach.
Index Terms:
VIdeo coding,Videos,Threshold analysis,Learning systems,Sequential analysis,Multimedia communication,multiscale sequence matching,TRECVID-CBCD,multimedia,video copy detection,scalable cascading,complementary detectors,soft threshold learning
Yonghong Tian, Tiejun Huang, Menglin Jiang, Wen Gao, "Video Copy-Detection and Localization with a Scalable Cascading Framework," IEEE Multimedia, vol. 20, no. 3, pp. 72-86, July-Sept. 2013, doi:10.1109/MMUL.2012.62
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