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As an alternative to the watermarking approach, content-based copy detection (CBCD) recently has become a promising technique for video monitoring and copyright protection. In this paper, a novel CBCD framework is proposed. Firstly, robust global feature (DCT), local features (SIFT) and audio feature (EDF) are first combined to describe video and audio contents, and the density sampling method is used to improve the generation of visual codebook. Secondly, picture-in-picture (PIP) detection algorithm is introduced to find the PIP transformation of videos. Meanwhile, a video matching method based on visual codebook is presented to calculate the similarity of copy videos and locality-sensitive hashing local (LSH) method is used for DCT indexing. 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 2011.