Content-based video copy detection in large databases: a local fingerprints statistical similarity search approach

Recent methods based on interest points and local fingerprints have been proposed to perform robust CBCD (content-based copy detection) of images and video. They include two steps: the search for similar local fingerprints in the database (DB) and a voting strategy that merges all the local results in order to perform a global decision. In most image or video retrieval systems, the search for similar features in the DB is performed by a geometrical query in a multidimensional index structure. Recently, the paradigm of approximate k-nearest neighbors query has shown that trading quality for time can be widely profitable in that context. In this paper, we evaluate a new approximate search paradigm, called Statistical Similarity Search (S3 ) in a complete CBCD scheme based on video local fingerprints. Experimental results show that these statistical queries allow high performance gains compared to classical -range queries and that trading quality for time during the search does not degra...

• Type: Conference
• Year: 2005
• Where: ICIP
• Authors: Alexis Joly, Carl Frélicot, Olivier Buisson
• Keywords: Approximate K-nearest Neighbors, Approximate Search Paradigm, ICIP 2005, Image Processing, Similar Local Fingerprints, Statistical Similarity Search, Video Local Fingerprints