Robust image hashing with multidimensional scaling

Multidimensional scaling (MDS) is an efficient technique for data analysis, and has been successfully applied in data visualization, object retrieval, data clustering, and so on. However, its use in image hashing is rarely discussed yet. In this study, we investigate the use of MDS in image hashing and propose an MDS-based hashing algorithm resistant to any-angle rotation. The proposed algorithm extracts a rotation-invariant feature matrix with log-polar transform and discrete Fourier transform from the normalized image, and learns a compact and discriminative representation from the feature matrix by MDS. Experiments with 3845 images are carried out and the results demonstrate that the proposed algorithm is robust to many content-preserving operations, including any-angle rotation, and reaches good discrimination. Receiver operating characteristics (ROC) curve comparisons illustrate that the proposed algorithm outperforms some state-of-the-art algorithms in classification with respect to robustness and discrimination.