Deep Discrete Cross-Modal Hashing for Cross-Media Retrieval

Cross-modal hashing has drawn increasing research interests in multimedia retrieval due to the explosive growth of multimedia big data. It is such a challenging topic due to the heterogeneity gap and high storage cost. However, most of the previous methods based on conventional linear projections and relaxation scheme fail to capture the nonlinear relationship among samples and suffers from large quantization loss, which result in an unsatisfactory performance of cross-modal retrieval. To address these issues, this paper is dedicated to learning discrete nonlinear hash functions by deep learning. A novel framework of cross-modal deep neural networks is proposed to learn binary codes directly. We formulate the similarity preserving in the framework, and also bit-independent as well as binary constraints are imposed on the hash codes. Specifically, we consider intra-modality similarity preserving at each hidden layer of the networks. Inter-modality similarity preserving is formulated by the output of each individual network. By so doing, the cross correlation can be encoded into the network training (i.e. hash functions learning) by back propagation algorithm. The final objective is solved by alternative optimization in an iterative fashion. Experimental results on four datasets i.e. NUS-WIDE, MIR Flickr, Pascal VOC, and LabelMe demonstrate the effectiveness of the proposed method, which is significantly superior to state-of-the-art cross-modal hashing approaches. (C) 2018 Elsevier Ltd. All rights reserved.