Unsupervised deep hashing via binary latent factor models for large-scale cross-modal retrieval

Abstract

Despite its great success, matrix factorization based cross-modality hashing suffers from two problems: 1) there is no engagement between feature learning and binarization; and 2) most existing methods impose the relaxation strategy by discarding the discrete constraints when learning the hash function, which usually yields suboptimal solutions. In this paper, we propose a multimodal hashing framework, termed Unsupervised Deep Cross-Modal Hashing (UDCMH), for multimodal data search via integrating deep learning and matrix factorization with binary latent factor models. On one hand, our unsupervised deep learning framework enables the feature learning to be jointly optimized with the binarization. On the other hand, the hashing system based on the binary latent factor models can generate unified binary codes by solving a discrete-constrained objective function directly with no need for relaxation. Moreover, novel Laplacian constraints are incorporated into the objective function, which allow to preserve not only the nearest neighbors that are commonly considered in the literature but also the farthest neighbors of data. Extensive experiments on multiple datasets highlight the superiority of the proposed framework over several state-of-the-art baselines.