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Available from: Xiaoming Nan, Jun 26, 2015
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    ABSTRACT: Content-based video copy detection aims at deciding whether there is a common segment between the query video and the video in the database. In this paper, a copy detection system is proposed based on local features that can deal with most video transformations and realize video searching in the database by using inverted file. Local features are first extracted and then clustered to visual words as index of the inverted file. The voting strategy makes use of the property of temporal consistence. The experimental results indicate that these visual features are robust and the searching in database is feasible.
    Intelligent Computing and Intelligent Systems, 2009. ICIS 2009. IEEE International Conference on; 12/2009
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    ABSTRACT: Searching of video clip from TV streams has significant commercial values, for example, the application of advertisement tracking in different TV channels. In this paper, a segment-based advertisement searching method is proposed. Robust visual features are first extracted to overcome some video transformations, and then two search strategies are presented for long AD clips and short ones. The experimental results indicate that the average search time of one query clip from a 24-hour TV stream is about 1 second, and the mean recall for 9 channels exceeds 98% while 100% precision is achieved. The evaluation results of copy detection task at TRECVID show the robustness and effectiveness.
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    ABSTRACT: Content-based copy detection (CBCD) recently has appeared a promising technique for video monitoring and copyright protection. In this paper, a novel framework for CBCD is proposed. Robust global features and local Speeded Up Robust Features (SURF) are first combined to describe video contents, and the density sampling method is proposed to improve the generation of visual codebook. Secondly, Smith-Waterman algorithm is introduced to find the similar video segments, meanwhile, a video matching method based on visual codebook is proposed to calculate the similarity of copy videos. Finally, a hierarchical fusion scheme is used to refine the detection results. Experiments on TRECVID dataset show that the proposed framework gives better results than the average results of CBCD task in TRECVID 2008.