A Rotation Invariant Descriptor for Robust Video Copy Detection

A large amount of videos on the Internet are generated from authorized sources by various kinds of transformations. Many works are proposed for robust description of video, which lead to satisfying matching qualities on Content Based Copy Detection (CBCD) issue. However, the trade-off of efficiency and effectiveness is still a problem among the state-of-the-art CBCD approaches. In this paper, we propose a novel frame-level descriptor for video. Firstly, each selected frame is partitioned into certain rings. Then the Histogram of Oriented Gradient (HOG) and the Relative Mean Intensity (RMI) are calculated as the original features. We finally fuse these two features by summing HOGs with RMIs as the corresponding weights. The proposed descriptor is succinct in concept, compact in structure, robust for rotation like transformations and fast to compute. Experiments on the CIVR’07 Copy Detection Corpus and the Video Transformation Corpus show improved performances both on matching quality and executive time compared to the pervious approaches.