A novel image hashing with low-rank sparse matrix decomposition and feature distance

Image hashing is an efficient technique of image processing for various applications, such as retrieval, copy detection and authentication. In this paper, we design a novel image hashing algorithm using LRSMD (low-rank sparse matrix decomposition). Firstly, an input image is preprocessed by interpolation, Gaussian blur and color space conversion. Next, the preprocessed image is fed into the LRSMD for learning a low-rank matrix. Then, statistical features of non-overlapping blocks in the low-rank matrix are extracted. Finally, the hash code is obtained by calculating feature distances. Various experiments are done on public datasets to demonstrate the robustness and discrimination of the proposed algorithm. The results show that the proposed algorithm outperforms several advanced algorithms in balancing the performances of robustness and discrimination.