Weber Binarized Statistical Image Features (WBSIF) based video copy detection

This paper presents a new video copy detection system based on testing similarities between textural feature vectors which are extracted from videos. Herein, the proposed method is based on Weber Binarized Statistical Image Features (WBSIF) which is an interior improvement of Weber Local Descriptor (WLD). Actually, the orientation gradient in WLD is substituted by a recent Binarized Statistical Image Features (BSIF) as a local textural descriptor. The WBSIF approach is tested on three databases and evaluated through several attacks. Moreover, the proposed method is compared to the recent existing approaches, especially those mostly used in the literature, which are based on the binary pattern descriptors. The obtained results outline the robustness and the effectiveness of the proposed video copy detection system in terms of precision, recall, Fscore, accuracy and collision test. This study shows clearly a noteworthy performance of the proposed scheme against currently existing techniques.