  Recent years have witnessed wide application of hashing for large-scale image
retrieval. However, most existing hashing methods are based on hand-crafted
features which might not be optimally compatible with the hashing procedure.
Recently, deep hashing methods have been proposed to perform simultaneous
feature learning and hash-code learning with deep neural networks, which have
shown better performance than traditional hashing methods with hand-crafted
features. Most of these deep hashing methods are supervised whose supervised
information is given with triplet labels. For another common application
scenario with pairwise labels, there have not existed methods for simultaneous
feature learning and hash-code learning. In this paper, we propose a novel deep
hashing method, called deep pairwise-supervised hashing(DPSH), to perform
simultaneous feature learning and hash-code learning for applications with
pairwise labels. Experiments on real datasets show that our DPSH method can
outperform other methods to achieve the state-of-the-art performance in image
retrieval applications.
