An efficient dual semantic preserving hashing for cross-modal retrieval

Abstract

Hashing methods have recently received widespread attention due to their flexibility and effectiveness for cross-modal retrieval tasks. However, most existing cross-modal hashing methods have some challenging problems, in particular, effective exploitation of semantic information and learning discriminative hash codes. To address these challenges, we propose an efficient Dual Semantic Preserving Hashing (DSPH) method, which first leverages matrix factorization to obtain low-level latent semantic representations of different modalities and remove redundant information. To enhance the discriminative capability of hash codes, we preserve the high-level pairwise semantics and the learned low-level latent semantics into the unified hash codes. Finally, DSPH adopts discrete optimization strategy to learn the hash codes directly. Experimental results on three benchmark datasets demonstrate that the proposed DSPH method outperforms many state-of-the-art cross-modal hashing methods in terms of retrieval accuracy, especially when dealing with short hash code.