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Deep Supervised Hashing with Information Loss

  • Xueni Zhang
  • Lei Zhou
  • Xiao Bai
  • Edwin Hancock
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11004)

Abstract

Recently, deep neural networks based hashing methods have greatly improved the image retrieval performance by simultaneously learning feature representations and binary hash functions. Most deep hashing methods utilize supervision information from semantic labels to preserve the distance similarity within local structures, however, the global distribution is ignored. We propose a novel deep supervised hashing method which aims to minimize the information loss during low-dimensional embedding process. More specifically, we use Kullback-Leibler divergences to constrain the compact codes having a similar distribution with the original images. Experimental results have shown that our method outperforms current stat-of-the-art methods on benchmark datasets.

Keywords

Hashing Image retrieval KL divergence 

Notes

Acknowledgement

This work was supported by the National Natural Science Foundation of China project no. 61772057, in part by Beijing Natural Science Foundation project no. 4162037, and the support funding from State Key Lab. of Software Development Environment.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Xueni Zhang
    • 1
  • Lei Zhou
    • 1
  • Xiao Bai
    • 1
  • Edwin Hancock
    • 2
  1. 1.School of Computer Science and Engineering and Beijing Advanced Innovation Center for Big Data and Brain ComputingBeihang UniversityBeijingChina
  2. 2.Department of Computer ScienceUniversity of YorkYorkUK

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