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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
