Advertisement

Supervised Locality Preserving Hashing

  • Xiao Zhou
  • Zhihui Lai
  • Yudong Chen
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 849)

Abstract

Hashing methods are becoming increasingly popular because they can achieve fast retrieval of large-scale data by representing the images with binary codes. However, the traditional hashing methods tend to obtain the binary codes by relaxing the discrete problems which greatly increase the information loss. In this paper, we propose a novel hash learning method, called Supervised Locality Preserving Hashing (SLPH) for image retrieval. Different from the traditional two-steps methods which learn low-dimensional features and binary codes of the data separately, we directly obtain the binary codes and thus reduce the information loss. Besides, we add graph-regularized learning on the designed model to avoid over-fitting and improve the performance. Experiments on two benchmark databases show that the proposed SLPH performs better than some state-of-the-art methods.

Keywords

Linear regression Binary code Feature extraction Hash learning 

Notes

Acknowledgements

This work was supported in part by the Natural Science Foundation of China (Grant 61573248, Grant 61773328, Grant 61732011 and Grant 61703283), Research Grant of The Hong Kong Polytechnic University (Project Code: G-UA2B), China Postdoctoral Science Foundation (Project 2016M590812 and Project 2017T100645), the Guangdong Natural Science Foundation (Project 2017A030313367 and Project 2017A030310067), and Shenzhen Municipal Science and Technology Innovation Council (No. JCYJ20170302153434048 and No. JCYJ20160429182058044).

References

  1. 1.
    Gionis A., Indyk P., Motwani R.: Similarity search in high dimensions via hashing. In: Proceedings of the 25th International Conference on Very Large Data Bases, 1999, pp. 518–529 (1999)Google Scholar
  2. 2.
    Datar M., Immorlica N., Indyk P., Mirrokni V.S.: Locality-sensitive hashing scheme based on p-stable distributions. In: Twentieth Symposium on Computational Geometry, vol. 34, No. 2, pp. 253–262 (2004)Google Scholar
  3. 3.
    Charikar M.S.: Similarity estimation techniques from rounding algorithms. In: Proceedings of the 34th STOC, 2002, pp. 380–388 (2002)Google Scholar
  4. 4.
    Kulis, B., Jain, P., Grauman, K.: Fast similarity search for learned metrics. IEEE Trans. Pattern Anal. Mach. Intell. 31, 2143–2157 (2009)CrossRefGoogle Scholar
  5. 5.
    Kulis B., Grauman K.: Kernelized locality-sensitive hashing for scalable image search. In: IEEE International Conference on Computer Vision, 2010, pp. 2130–2137 (2010)Google Scholar
  6. 6.
    Raginsky M.: Locality-sensitive binary codes from shift-invariant kernels. In: Advances in Neural Information Processing Systems, pp. 1509–1517 (2009)Google Scholar
  7. 7.
    Wang, J., Kumar, S., Chang, S.F.: Semi-supervised hashing for large-scale search. IEEE Trans. Pattern Anal. Mach. Intell. 34, 2393–2406 (2012)CrossRefGoogle Scholar
  8. 8.
    Gong, Y., Lazebnik, S., Gordo, A., Perronnin, F.: Iterative quantization: a procrustean approach to learning binary bodes for large-scale image retrieval. IEEE Trans. Pattern Anal. Mach. Intell. 35, 2916–2929 (2013)CrossRefGoogle Scholar
  9. 9.
    Kong W., Li W.J.: Isotropic hashing. In: International Conference on Neural Information Processing Systems, 2012, pp. 1646–1654 (2012)Google Scholar
  10. 10.
    Norouzi M., Fleet D.J.: Minimal loss hashing for compact binary codes. In: International Conference on Machine Learning, 2011, pp. 353–360 (2011)Google Scholar
  11. 11.
    Strecha, C., Bronstein, A., Bronstein, M., Fua, P.: LDAHash: improved matching with smaller descriptors. IEEE Trans. Pattern Anal. Mach. Intell. 34, 66–78 (2012)CrossRefGoogle Scholar
  12. 12.
    Wang J., Liu W., Sun A.X., Jiang Y.G.: Learning hash codes with listwise supervision. In: IEEE International Conference on Computer Vision, 2014, pp. 3032–3039 (2014)Google Scholar
  13. 13.
    Lin G., Shen C., Shi Q., Hengel A., Suter D.: Fast supervised hashing with decision trees for high-dimensional data. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2014, pp. 1971–1978 (2014)Google Scholar
  14. 14.
    Kulis B., Darrell T.: Learning to hash with binary reconstructive embeddings. In: Proceedings of the 22nd International Conference on Neural Information Processing Systems, 2009, pp. 1042–1050 (2009)Google Scholar
  15. 15.
    Buisson O., Buisson, O.: Random maximum margin hashing. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2011, pp. 873–880 (2011)Google Scholar
  16. 16.
    Chang S.F.: Supervised hashing with kernels. Supervised hashing with kernels. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2012, pp. 2074–2081 (2012)Google Scholar
  17. 17.
    Weiss Y., Torralba A., Fergus R.: Spectral hashing. In: Proceedings of the 21st International Conference on Neural Information Processing Systems, 2008, pp. 1753–1760 (2008)Google Scholar
  18. 18.
    Belkin M., Niyogi P.: Laplacian Eigenmaps for dimensionality reduction and data representation. MIT Press (2003)Google Scholar
  19. 19.
    Liu W., Kumar S., Kumar S., Chang S.F.: Discrete graph hashing. In: Proceedings of the 27th International Conference on Neural Information Processing Systems, 2014, pp. 3419–3427 (2014)Google Scholar
  20. 20.
    Shen F., Shen C., Shi Q., Hengel A., Tang Z.: Inductive hashing on manifolds. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2013, pp. 1562–1569 (2013)Google Scholar
  21. 21.
    Shen, F., Shen, C., Shi, Q., Hengel, A., Tang, Z., Shen, H.T.: Hashing on nonlinear manifolds. IEEE Trans. Image Process. 24, 1839–1851 (2015)MathSciNetCrossRefGoogle Scholar
  22. 22.
    He, X., Niyogi, P.: Locality preserving projections. Adv. Neural. Inf. Process. Syst. 16, 186–197 (2003)Google Scholar
  23. 23.
    Shen F., Shen C., Liu W., Shen H.T.: Supervised discrete hashing. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 37–45 (2015)Google Scholar
  24. 24.
    Gui, J., Liu, T., Sun, Z., Tao, D., Tan, T.: Fast supervised discrete hashing. IEEE Trans. Pattern Anal. Mach. Intell. 40(2), 490–496 (2018)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.The College of Computer Science and Software EngineeringShenzhen UniversityShenzhenChina

Personalised recommendations