Toward a Multi-Feature Approach to Content-Based Copy Detection

Video Content-Based Copy Detection (CBCD) is an emergent research field which is targeted to the identification of modified copies of an original clip in a given dataset, e.g., on the Internet. As opposed to digital watermarking, the content itself is used to uniquely identify the video through the extraction of features that need to be robust against a certain set of predetermined video attacks. This paper advocates the use of multiple features together with detection performance estimation to construct a flexible video signature instead of a fixed, single feature based one. To combine diverse features, a normalized linear combination is also proposed. The system performance boost is evaluated through the MPEG Video Signature Core Experiment dataset and experimental results show how the proposed signature scheme can achieve impressive improvements with respect to the single feature approach.