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Advances in Multimedia
Volume 2017, Article ID 8961091, 10 pages
Research Article

Deep Binary Representation for Efficient Image Retrieval

1Institute of Image Communication and Network Engineering, Shanghai Jiao Tong University, Shanghai, China
2Future Medianet Innovation Center, Shanghai, China

Correspondence should be addressed to Li Song; nc.ude.utjs@il_gnos

Received 25 May 2017; Revised 31 July 2017; Accepted 7 September 2017; Published 12 November 2017

Academic Editor: XiangLong Liu

Copyright © 2017 Xuchao Lu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


With the fast growing number of images uploaded every day, efficient content-based image retrieval becomes important. Hashing method, which means representing images in binary codes and using Hamming distance to judge similarity, is widely accepted for its advantage in storage and searching speed. A good binary representation method for images is the determining factor of image retrieval. In this paper, we propose a new deep hashing method for efficient image retrieval. We propose an algorithm to calculate the target hash code which indicates the relationship between images of different contents. Then the target hash code is fed to the deep network for training. Two variants of deep network, DBR and DBR-v3, are proposed for different size and scale of image database. After training, our deep network can produce hash codes with large Hamming distance for images of different contents. Experiments on standard image retrieval benchmarks show that our method outperforms other state-of-the-art methods including unsupervised, supervised, and deep hashing methods.