International Conference on Multimedia Modeling

MultiMedia Modeling pp 337-348 | Cite as

SOMH: A Self-Organizing Map Based Topology Preserving Hashing Method

  • Xiao-Long Liang
  • Xin-Shun Xu
  • Lizhen Cui
  • Shanqing Guo
  • Xiao-Lin Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9516)

Abstract

Hashing based approximate nearest neighbor search techniques have attracted considerable attention in media search community. An essential problem of hashing is to keep the neighborhood relationship while doing hashing map. In this paper, we propose a self-organizing map based hashing method–SOMH, which cannot only keep similarity relationship, but also preserve topology of data. Specifically, in SOMH, self-organizing map is introduced to map data points into hamming space. In this framework, in order to make it work well on short and long binary codes, we propose a relaxed version of SOMH and a product space SOMH, respectively. For the optimization problem of relaxed SOMH, we also present an iterative solution. To test the performance of SOMH, we conduct experiments on two benchmark datasets–SIFT1M and GIST1M. Experimental results show that SOMH can outperform or is comparable to several state-of-the-arts.

Keywords

Hashing Self-organizing map Media research Approximate nearest neighbor search 

Notes

Acknowledgments

This work is partially supported by National Natural Science Foundation of China (61173068, 61573212, 61572295), Program for New Century Excellent Talents in University of the Ministry of Education, the Key Science Technology Project of Shandong Province (2014GGD01063), the Independent Innovation Foundation of Shandong Province (2014CGZH1106) and the Shandong Provincial Natural Science Foundation (ZR2014FM020, ZR2014FM031).

References

  1. 1.
    Wang, J., Shen, H.T., Song, J., Ji, J.: Hashing for similarity search: a survey. arXiv (2014)Google Scholar
  2. 2.
    Andoni, A., Indyk, P.: Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions. In: Proceedings of FOCS, pp. 459–468 (2006)Google Scholar
  3. 3.
    Athitsos, V., Potamias, M., Papapetrou, P., Kollios, G.: Nearest neighbor retrieval using distance-based hashing. In: Proceedings of ICDE, pp. 327–336 (2008)Google Scholar
  4. 4.
    Jin, Z., Li, C., Lin, Y., Cai, D.: Density sensitive hashing. IEEE Trans. Cybern. 44(8), 1362–1371 (2013)CrossRefGoogle Scholar
  5. 5.
    Zhen, Y., Yeung, D.Y.: A probabilistic model for multimodal hash function learning. In: Proceedings of KDD, pp. 940–948 (2012)Google Scholar
  6. 6.
    Wang, J., Kumar, S., Chang, S.-F.: Sequential projection learning for hashing with compact codes. In: Proceedings of CVPR, pp. 3424–3431 (2010)Google Scholar
  7. 7.
    Kim, S., Choi, S.: Semi-supervised discriminant hashing. In: Proceedings of ICDM, pp. 1122–1127 (2011)Google Scholar
  8. 8.
    Wang, S.S., Huang, Z., Xu, X.-S.: A multi-label least-squares hashing for scalable image search. In: Proceedings of SDM 2015, pp. 954–962 (2015)Google Scholar
  9. 9.
    Xu, B., Bu, J., Lin, Y., Chen, C., He, X., Cai, D.: Harmonious hashing. In: Proceedings of IJCAI, pp. 1820–1826 (2013)Google Scholar
  10. 10.
    Gionis, A., Indyk, P., Motwani, R.: Similarity search in high dimensions via hashing. In: Proceedings of VLDB, pp. 518–529 (1999)Google Scholar
  11. 11.
    Raginsky, M., Lazebnik, S.: Locality-sensitive binary codes from shift-invariant kernels. In: NIPS, vol. 22, pp. 1509–1517 (2009)Google Scholar
  12. 12.
    Weiss, Y., Torralba, A., Fergus, R.: Spectral hashing. In: NIPS, vol. 21, pp. 1753–1760 (2008)Google Scholar
  13. 13.
    Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput. 15, 1373–1396 (2003)MATHCrossRefGoogle Scholar
  14. 14.
    Gong, Y., Lazebnik, S.: Iterative quantization: a procrustean approach to learning binary codes. In: Proceedings of CVPR, pp. 817–824 (2011)Google Scholar
  15. 15.
    Irie, G., Li, Z., Wu, X.M., Chang, S.F.: Locally linear hashing for extracting non-linear manifolds. In: Proceedings of CVPR, pp. 2123–2130 (2014)Google Scholar
  16. 16.
    Shen, F., Shen, C., Shi, Q., van den Hengel, A.: Inductive hashing on manifolds. In: Proceedings of CVPR, pp. 1562–1569 (2013)Google Scholar
  17. 17.
    He, K., Wen, H., Sun, J.: K-means hashing: an affinity-preserving quantization method for learning binary compact codes. In: Proceedings of CVPR, pp. 2938–2945 (2013)Google Scholar
  18. 18.
    Johnson, T.: Networks. In: Klouche, T., Noll, T. (eds.) MCM 2007. CCIS, vol. 37, pp. 311–317. Springer, Heidelberg (2009) CrossRefGoogle Scholar
  19. 19.
    Norouzi, M., Fleet, D.J.: Cartesian K-means. In: Proceedings of CVPR, pp. 3017–3024 (2013)Google Scholar
  20. 20.
    Ge, T., He, K., Ke, Q., Sun, J.: Optimized product quantization for approximate nearest neighbor search. In: Proceedings of CVPR, pp. 2946–2953 (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Xiao-Long Liang
    • 1
  • Xin-Shun Xu
    • 1
  • Lizhen Cui
    • 1
  • Shanqing Guo
    • 1
  • Xiao-Lin Wang
    • 1
  1. 1.School of Computer Science and TechnologyShandong UniversityJinanChina

Personalised recommendations