Supervised Consistent And Specific Hashing

Most existing methods seek for the common semantics using different projections for different modalities, which isolates the intrinsic relationships among different modalities. Besides, to avoid the large quantization error, some of them adopt the discrete cyclic coordinate descent schemes which are usually time-consuming. To address these issues, we present a novel hashing method, namely Supervised Consistent and Specific Hashing (SCSH), for cross-modal retrieval. We explicitly decompose the mapping matrices into consistent part and modality-specific ones. Specifically, consistency excavates the semantic shared by different modalities, whereas specificity captures private properties for each modality. Different from prior works, SCSH can discover the intrinsic semantic shared among different modalities more accurately. Moreover, by regressing the semantic labels to hash codes, SCSH can further promote the discriminative power of hash codes and significantly accelerate the hashing learning process. Extensive experiments on three widely used datasets demonstrate that the proposed SCSH outperforms other state-of-the-art methods.