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

Content Provider	CiteSeerX
Author	Joly, Alexis Buisson, Olivier
Description	Recent methods based on interest points and local fingerprints have been proposed to perform robust CBCD (content-based copy de-tection) 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 geomet-rical 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 con-text. 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 degrade seriously the global ro-bustness of the system, even with very large DBs including more than 20,000 hours of video. 1. In Proceedings of the IEEE International Conference on Image Processing (ICIP
File Format	PDF
Language	English
Access Restriction	Open
Subject Keyword	Video Local Fingerprint Content-based Copy De-tection Local Result New Approximate Search Paradigm Similar Feature Approximate K-nearest Neighbor Query Geomet-rical Query Global Ro-bustness Recent Method Local Fingerprint Video Retrieval System Trading Quality Complete Cbcd Scheme High Performance Gain Large Database Voting Strategy Statistical Query Statistical Similarity Search Interest Point Similar Local Fingerprint Multidimensional Index Structure Classical Range Query Robust Cbcd Global Decision Experimental Result Large Db Content-based Video Copy Detection
Content Type	Text
Resource Type	Article