Perceptual video hashing based on Tucker decomposition with application to indexing and retrieval of near-identical videos

The perceptual video hash function defines a feature vector that characterizes a video depending on its perceptual contents. This function must be robust to the content preserving manipulations and sensitive to the content changing manipulations. In the literature, the subspace projection techniques such as the reduced rank PARAllel FACtor analysis (PARAFAC), have been successfully applied to extract perceptual hash for the videos. We propose a robust perceptual video hash function based on Tucker decomposition, a multi-linear subspace projection method. We also propose a method to find the optimum number of components in the factor matrices of the Tucker decomposition. The Receiver Operating Characteristics (ROC) curves are used to evaluate the performance of the proposed algorithm compared to the other state-of-the-art projection techniques. The proposed algorithm shows superior performance for most of the image processing attacks. An application for indexing and retrieval of near-identical videos is developed using the proposed algorithm and the performance is evaluated using average recall/precision curves. The experimental results show that the proposed algorithm is suitable for indexing and retrieval of near-identical videos.