Deep supervised fused similarity hashing for cross-modal retrieval

The need for cross-modal retrieval increases significantly with the rapid growth of multimedia information on the Internet. However, most of existing cross-modal retrieval methods neglect the correlation between label similarity and intra-modality similarity in common semantic subspace training, which makes the trained common semantic subspace unable to preserve semantic similarity of original data effectively. Therefore, a novel cross-modal hashing method is proposed in this paper, namely, Deep Supervised Fused Similarity Hashing (DSFSH). The DSFSH mainly consists of two parts. Firstly, a fused similarity method is proposed to exploit the intrinsic inter-modality correlation of data while preserving the intra-modality relationship of data at the same time. Secondly, a novel quantization max-margin loss is proposed. The gap between cosine similarity and Hamming similarity is closed by minimizing this loss. Extensive experimental results on three benchmark datasets show that the proposed method yields better retrieval performance comparing to state-of-the-art methods.