Robust Video Hashing with Non-negative Tensor Factorization for Copy Detection

Abstract

Copy detection is a key task of video copyright protection. This paper presents a robust video hashing with non-negative tensor factorization (NTF) for copy detection. In the presented video hashing scheme, secondary frames are computed from the preprocessed video by assigning weights to all frames within a video group based on color entropy. Next, the secondary frames are fed into the pre-trained MobileNetV2 and then NTF is exploited to compress the three-order tensor constructed by stacking the output feature maps for hash construction. Experiments conducted on publicly available video datasets indicate that the presented hashing scheme outperforms the evaluated hashing schemes in the performances of classification and copy detection.