To address the problems that hand-engineering visual features can't be optimally compatible with the Hash coding process and existing Hash methods can't differentiate images semantics information

a learning method of binary Hashing based on deep convolutional neural networks is proposed.The basic idea is to add a Hash layer into the deep residual network and to learn simultaneously image features and Hash functions.Meanwhile

we propose a more compact hierarchical Hashing structure to extract features closer to semantics information of images.Experimental results of MNIST

CIFAR-10 and NUS-WIDE datasets show that the method is superior to existing Hashing methods.This method not only unifies the process of feature learning and Hash coding

and at the same time

the deep residual network is able to get features closer to image semantics.Thus the retrieval accuracy is improved.