  In recent years, hashing methods have been proved to be effective and
efficient for the large-scale Web media search. However, the existing general
hashing methods have limited discriminative power for describing fine-grained
objects that share similar overall appearance but have subtle difference. To
solve this problem, we for the first time introduce the attention mechanism to
the learning of fine-grained hashing codes. Specifically, we propose a novel
deep hashing model, named deep saliency hashing (DSaH), which automatically
mines salient regions and learns semantic-preserving hashing codes
simultaneously. DSaH is a two-step end-to-end model consisting of an attention
network and a hashing network. Our loss function contains three basic
components, including the semantic loss, the saliency loss, and the
quantization loss. As the core of DSaH, the saliency loss guides the attention
network to mine discriminative regions from pairs of images. We conduct
extensive experiments on both fine-grained and general retrieval datasets for
performance evaluation. Experimental results on fine-grained datasets,
including Oxford Flowers-17, Stanford Dogs-120, and CUB Bird demonstrate that
our DSaH performs the best for fine-grained retrieval task and beats the
strongest competitor (DTQ) by approximately 10% on both Stanford Dogs-120 and
CUB Bird. DSaH is also comparable to several state-of-the-art hashing methods
on general datasets, including CIFAR-10 and NUS-WIDE.
