An Approach to: Identify and Retrieve Subsequence Matching Frames from Videos

Retrieving a duplicate video is becoming more and more important with the exponential growth. Though various approaches have been proposed to manipulate a large video database, effective video indexing and retrieval, these are mainly focusing on the retrieval accuracy while infeasible to query on Web scale video database in real time. This paper proposes a novel method for efficiency and scalability issues for similar frames through video. We proposed a novel method to inserting a distinct pattern into the video stream, video copy detection techniques match contentbased signatures to detect copies of video. Existing typical contentbased copy detection schemes have relied on image matching. To effectively match the video sequences with a low computational load, we use the key frames extracted by the cumulative directed divergence and compare the set of key frames using the novel method and g-l technique. Motion, intensity and color-based signatures are compared in the context of copy detection. Results are reported on detecting copies of movie clips. To demonstrate the effectiveness and efficiency of the proposed method, we evaluate its performance on an open video data set containing about 10K videos and compare it with four existing methods in terms of precision and time complexity.