A video copy detection algorithm based on two-level features measure

Authors:
Jie Dang;Bei Lu;Jin-liang Yao
Affiliations:
Institute of Computer Application Technology, Hangzhou Dianzi University, Hangzhou, China;Institute of Computer Application Technology, Hangzhou Dianzi University, Hangzhou, China;Institute of Computer Application Technology, Hangzhou Dianzi University, Hangzhou, China
Venue:
AICI'12 Proceedings of the 4th international conference on Artificial Intelligence and Computational Intelligence
Year:
2012

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Abstract

Video copy detection is a crucial technique for copyright protection. However, the main disadvantages of most existing approaches are high computational cost and low robustness. In this paper, we consider videos as a set of shots and propose a video copy detection framework that extracts video shots' overall features and spatiotemporal features. To effectively enhance the accuracy of final results, a coarse-to-precise filtration approach is proposed in this paper. In the coarse stage, the video copy shot retrieval is preformed by extracting the features of a video shot based on spatial-chromatic histograms. In the refined stage, the spatiotemporal features improved by quantization encoding are applied to the final verification. The combination of FLANN and "as early as possible to stop" process is adopted to accelerate the detection process in the coarse stage. The experimental results show that the proposed approach is effective in detecting video copies with promising precision and recall rate.