Deep supervised hashing with hard example pairs optimization for image retrieval

Compared with the traditional hashing methods, deep hashing methods generate hash codes with rich semantic information and greatly improve the performances in the image retrieval field. However, it is unsatisfied for current deep hashing methods to predict the similarity of hard example pairs. There exist two main factors affecting the ability of learning these pairs, which are weak key features extraction and the shortage of hard example pairs. In this paper, we give a novel end-to-end model to extract the key feature and obtain hash code with the accurate semantic information. In addition, we redesign an indicator to assess the hard degree of pairs and update penalty weights of them in the proposed hard pair-wise loss. It effectively alleviates the shortage problem. Experimental results on CIFAR-10 and NUS-WIDE demonstrate that our model outperformances the mainstream hashing-based image retrieval methods.