A novel CBCD scheme based on local features category
WISM'12 Proceedings of the 2012 international conference on Web Information Systems and Mining
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Motion features contribute significant information about a video content. This paper highlights a novel CBCD (Content-Based Copy Detection) approach, by incorporating several motion activity features. First, we extract both temporal and spatial motion features to describe overall activity of a video sequence. Second, we combine these features in a feasible manner, to generate robust video fingerprints. Third, clustering based pruned search is utilized for similarity matching instead of direct searching of video fingerprints. The proposed system is tested on TRECVID-2007 data set and the results demonstrate the effectiveness of the proposed system against several transformations such as random noise, fast forward, pattern insertion, cropping and picture-inside-picture.