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Efficient Supervised Hashing via Exploring Local and Inner Data Structure

  • Shiyuan He
  • Guo Ye
  • Mengqiu Hu
  • Yang YangEmail author
  • Fumin Shen
  • Heng Tao Shen
  • Xuelong Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10538)

Abstract

Recent years have witnessed the promising capacity of hashing techniques in tackling nearest neighbor search because of the high efficiency in storage and retrieval. Data-independent approaches (e.g., Locality Sensitive Hashing) normally construct hash functions using random projections, which neglect intrinsic data properties. To compensate this drawback, learning-based approaches propose to explore local data structure and/or supervised information for boosting hashing performance. However, due to the construction of Laplacian matrix, existing methods usually suffer from the unaffordable training cost. In this paper, we propose a novel supervised hashing scheme, which has the merits of (1) exploring the inherent neighborhoods of samples; (2) significantly saving training cost confronted with massive training data by employing approximate anchor graph; as well as (3) preserving semantic similarity by leveraging pair-wise supervised knowledge. Besides, we integrate discrete constraint to significantly eliminate accumulated errors in learning reliable hash codes and hash functions. We devise an alternative algorithm to efficiently solve the optimization problem. Extensive experiments on two image datasets demonstrate that our proposed method is superior to the state-of-the-arts.

Keywords

Supervised hashing Approximate anchor graph Inherent neighborhood 

Notes

Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Project 61572108, Project 61632007 and Project 61502081, the National Thousand-Young-Talents Program of China, and the Fundamental Research Funds for the Central Universities under Project ZYGX2014Z007 and Project ZYGX2015J055.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Shiyuan He
    • 1
  • Guo Ye
    • 1
  • Mengqiu Hu
    • 1
  • Yang Yang
    • 1
    Email author
  • Fumin Shen
    • 1
  • Heng Tao Shen
    • 1
  • Xuelong Li
    • 2
  1. 1.School of Computer Science and Engineering, Center for Future MediaUniversity of Electronic Science and Technology of ChinaChengduChina
  2. 2.State Key Laboratory of Transient Optics and Photonics, Center for OPTical IMagery Analysis and Learning (OPTIMAL)Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of SciencesBeijingChina

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