Index and Retrieve Multimedia Data: Cross-Modal Hashing by Learning Subspace Relation

Hashing methods have been extensively applied to efficient multimedia data indexing and retrieval on account of explosion of multimedia data. Cross-modal hashing usually learns binary codes by mapping multi-modal data into a common Hamming space. Most supervised methods utilize relation information like class labels as pairwise similarities of cross-modal data pair to narrow intra-modal and inter-modal gap. In this paper, we propose a novel supervised cross-modal hashing method dubbed Subspace Relation Learning for Cross-modal Hashing (SRLCH), which exploits relation information in semantic labels to make similar data from different modalities closer in the low-dimension Hamming subspace. SRLCH preserves the discrete constraints and nonlinear structures, while admitting a closed-form binary codes solution, which effectively enhances the training efficiency. An iterative alternative optimization algorithm is developed to simultaneously learn both hash functions and unified binary codes, indexing multimedia data in an efficient way. Evaluations in two cross-modal retrieval tasks on three widely-used datasets show that the proposed SRLCH outperforms most cross-modal hashing methods.