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 (S{sup}3) in a complete CBCD scheme based on video local fingerprints. Experimental results show that these statistical queries allow high performance gains compared to classical ε-range queries and that trading quality for time during the search does not degrade seriously the global robustness of the system, even with very large DBs including more than 20,000 hours of video.
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