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

Computer Vision and Pattern Recognition