Deep hashing learning networks
Guoqiang Zhong, Hui Xu, Yang Pan, Sijiang Wang, Junyu Dong · 2016
Hashing-based methods seek compact and efficient binary codes that preserve the similarity between data. For most existing hashing methods, an input (e.g. image) is first encoded as a vector of hand-crafted visual feature, followed by a hash projection and quantization step to obtain the compact binary vector. Most of hand-crafted features only encode low-level information of the input, the feature may not preserve semantic similarities of pairwise inputs. Meanwhile, the hash function learning process is independent with the feature representation, so that the feature may not be optimal for the hash projection. In this paper, we propose a supervised hashing learning method based on a well designed deep convolutional neural network, which tries to learn hashing code and compact representations of data simultaneously. Particularly, the proposed model learns binary codes by adding a compact sigmoid layer before the classifier layer. Experiments on several image data sets show that the proposed model outperforms other state-of-the-art hashing learning approaches.