Unsupervised cross-modal retrieval via Multi-modal graph regularized Smooth Matrix Factorization Hashing

The existing cross-modal hashing methods often encounter quantization loss which is caused by relaxing discrete hash codes in the process of cross-modal retrieval. To counter this problem, a Multi-modal graph regularized Smooth matrix Factorization Hashing (MSFH) approach is represented for unsupervised cross-modal retrieval. In the proposal framework, a smooth matrix generated by a control parameter is introduced into the matrix decomposition model, which can guarantee the sparsity of the dictionaries learned and the extracted common features at the same time, thus reducing the quantization loss in the hashing process. Furthermore, to preserve the topology of the original data, a multi-modal graph regularization term is drawn into the model, which consists of two parts. One is the intra-modal similarity graph which is used to preserve the geometric structure of each modality. The other is the inter-modal similarity graph reconstructed by the symmetric nonnegative matrix factorization, which is employed to soften the structure difference between modalities. The goal of MSFH is to learn unified hash-codes for multi-modal data in a shared latent semantic space in which the similarity of different modalities can be estimated effectively. And the corresponding experimental results on three benchmark data sets demonstrate the superiority of the proposed approach over several state-of-the-art cross-modality hashing approaches. (C) 2019 Elsevier B.V. All rights reserved.