Efficient discrete supervised hashing for large-scale cross-modal retrieval

Abstract

Supervised cross-modal hashing has gained increasing research interest on large-scale retrieval task owning to its satisfactory performance and efficiency. However, there are still some issues to be further addressed: (1) most of them fail to capture the inherent data structure effectively due to the complex correlations among heterogeneous data points; (2) most of them obtain continuous solutions firstly and then quantize the continuous solutions to generate hash codes directly, which causes large quantization error and consequent suboptimal retrieval performance; (3) most of them suffer from relatively high memory cost and computational complexity during training procedure, which makes them unscalable. In this paper, to address above issues, we propose a supervised hashing method for cross-modal retrieval dubbed Efficient Discrete Supervised Hashing (EDSH). Specifically, the sharing space learning with collective matrix factorization and semantic embedding with class labels are seamlessly integrated to learn hash codes. Therefore, the feature based similarities and semantic correlations are both preserved in hash codes, which makes the learned hash codes more discriminative. Then an efficient discrete optimal scheme is designed to handle the scalable issue. Instead of learning hash codes bit-by-bit, hash codes matrix can be obtained directly which is more efficient. Extensive experimental results on three public datasets show that our EDSH produces a superior performance in both accuracy and scalability over several existing cross-modal hashing approaches.