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)


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.


Supervised hashing Approximate anchor graph Inherent neighborhood 



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.


  1. 1.
    Bentley, J.L.: Multidimensional binary search trees used for associative searching. Commun. ACM 18(9), 509–517 (1975)CrossRefzbMATHGoogle Scholar
  2. 2.
    De Leeuw, J.: Applications of convex analysis to multidimensional scaling. Department of Statistics, UCLA (2005)Google Scholar
  3. 3.
    Gionis, A., Indyk, P., Motwani, R., et al.: Similarity search in high dimensions via hashing. In: VLDB, vol. 99, pp. 518–529 (1999)Google Scholar
  4. 4.
    Håstad, J.: Some optimal inapproximability results. J. ACM 48(4), 798–859 (2001)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Hu, M., Yang, Y., Shen, F., Zhang, L., Shen, H.T., Li, X.: Robust web image annotation via exploring multi-facet and structural knowledge. IEEE TIP 26(10), 4871–4884 (2017)Google Scholar
  6. 6.
    Krizhevsky, A., Hinton, G.: Learning multiple layers of features from tiny images (2009)Google Scholar
  7. 7.
    Kulis, B., Darrell, T.: Learning to hash with binary reconstructive embeddings. In: Proceedings of NIPS, pp. 1042–1050 (2009)Google Scholar
  8. 8.
    Kulis, B., Jain, P., Grauman, K.: Fast similarity search for learned metrics. IEEE TPAMI 31(12), 2143–2157 (2009)CrossRefGoogle Scholar
  9. 9.
    Liu, W., Mu, C., Kumar, S., Chang, S-F.: Discrete graph hashing. In: Proceedings of NIPS, pp. 3419–3427 (2014)Google Scholar
  10. 10.
    Liu, W., Wang, J., Ji, R., Jiang, Y-G., Chang, S-F.: Supervised hashing with kernels. In: Proceedings of CVPR, pp. 2074–2081 (2012)Google Scholar
  11. 11.
    Liu, W., Wang, J., Kumar, S., Chang, S-F.: Hashing with graphs. In: Proceedings of ICML, pp. 1–8 (2011)Google Scholar
  12. 12.
    Luo, Y., Yang, Y., Shen, F., Huang, Z., Zhou, P., Shen, H.T.: Robust discrete code modeling for supervised hashing. Pattern Recognit. (2017). doi: 10.1016/j.patcog.2017.02.034
  13. 13.
    Oliva, A., Torralba, A.: Modeling the shape of the scene: a holistic representation of the spatial envelope. IJCV 42(3), 145–175 (2001)CrossRefzbMATHGoogle Scholar
  14. 14.
    Shen, F., Shen, C., Liu, W., Shen, H.T.: Supervised discrete hashing. In: Proceedings of CVPR, pp. 37–45 (2015)Google Scholar
  15. 15.
    Shen, F., Yang, Y., Liu, L., Liu, W., Tao, D., Shen, H.T.: Asymmetric binary coding for image search. IEEE Trans. Multimedia 19(9), 2022–2032 (2017). doi: 10.1109/TMM.2017.2699863 CrossRefGoogle Scholar
  16. 16.
    Strecha, C., Bronstein, A.M., Bronstein, M.M., Fua, P.: LDAHash: improved matching with smaller descriptors. IEEE TPAMI 34(1), 66–78 (2012)CrossRefGoogle Scholar
  17. 17.
    Torralba, A., Fergus, R., Freeman, W.T.: 80 million tiny images: a large data set for nonparametric object and scene recognition. IEEE TPAMI 30(11), 1958–1970 (2008)CrossRefGoogle Scholar
  18. 18.
    Wang, J., Kumar, S., Chang, S.-F.: Semi-supervised hashing for large-scale search. IEEE TPAMI 34(12), 2393–2406 (2012)CrossRefGoogle Scholar
  19. 19.
    Weiss, Y., Torralba, A., Fergus, R.: Spectral hashing. In: Proceedings of NIPS, pp. 1753–1760 (2008)Google Scholar
  20. 20.
    Wolf, L., Hassner, T., Maoz, I.: Face recognition in unconstrained videos with matched background similarity. In: Proceedings of CVPR, pp. 529–534. IEEE (2011)Google Scholar
  21. 21.
    Yang, Y., Luo, Y., Chen, W., Shen, F., Shao, J., Shen, H.T.: Zero-shot hashing via transferring supervised knowledge. In: Proceedings of ACM MM, pp. 1286–1295 (2016)Google Scholar
  22. 22.
    Yang, Y., Shen, F., Huang, Z., Shen, H.T., Li, X.: Discrete nonnegative spectral clustering. In: IEEE TKDE (2017)Google Scholar
  23. 23.
    Yang, Y., Shen, F., Shen, H.T., Li, H., Li, X.: Robust discrete spectral hashing for large-scale image semantic indexing. IEEE TBD 1(4), 162–171 (2015)Google Scholar
  24. 24.
    Zhang, P., Zhang, W., Li, W.-J., Guo, M.: Supervised hashing with latent factor models. In: Proceedings of SIGIR, pp. 173–182 (2014)Google Scholar

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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