Skip to main content
SpringerLink
Log in
Book cover

Australasian Database Conference

ADC 2017: Databases Theory and Applications pp 110–122Cite as

Learning Robust Graph Hashing for Efficient Similarity Search

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10538))

Abstract

Unsupervised hashing has recently drawn much attention in efficient similarity search for its desirable advantages of low storage cost, fast search speed, semantic label independence. Among the existing solutions, graph hashing makes a significant contribution as it could effectively preserve the neighbourhood data similarities into binary codes via spectral analysis. However, existing graph hashing methods separate graph construction and hashing learning into two independent processes. This two-step design may lead to sub-optimal results. Furthermore, features of data samples may unfortunately contain noises that will make the built graph less reliable. In this paper, we propose a Robust Graph Hashing (RGH) to address these problems. RGH automatically learns robust graph based on self-representation of samples to alleviate the noises. Moreover, it seamlessly integrates graph construction and hashing learning into a unified learning framework. The learning process ensures the optimal graph to be constructed for subsequent hashing learning, and simultaneously the hashing codes can well preserve similarities of data samples. An effective optimization method is devised to iteratively solve the formulated problem. Experimental results on publicly available image datasets validate the superior performance of RGH compared with several state-of-the-art hashing methods.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    SIFT is employed as local feature.

References

  1. Agrawal, R., Faloutsos, C., Swami, A.N.: Efficient similarity search in sequence databases. In: Lomet, D.B. (ed.) FODO 1993. LNCS, vol. 730, pp. 69–84. Springer, Heidelberg (1993). doi:10.1007/3-540-57301-1_5

    Chapter  Google Scholar 

  2. Andoni, A., Indyk, P.: Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions. Commun. ACM 51(1), 117–122 (2008)

    Article  Google Scholar 

  3. Andoni, A., Razenshteyn, I.: Optimal data-dependent hashing for approximate near neighbors. In: Proceedings of Annual ACM Symposium on Theory of Computing, STOC 2015, pp. 793–801. ACM (2015)

    Google Scholar 

  4. Chua, T.S., Tang, J., Hong, R., Li, H., Luo, Z., Zheng, Y.: NUS-WIDE: a real-world web image database from national university of singapore. In: Proceedings of ACM International Conference on Image and Video Retrieval, CIVR 2009, pp. 48: 1–48: 9. ACM (2009)

    Google Scholar 

  5. Cost, S., Salzberg, S.: A weighted nearest neighbor algorithm for learning with symbolic features. Mach. Learn. 10(1), 57–78 (1993)

    Google Scholar 

  6. Datar, M., Immorlica, N., Indyk, P., Mirrokni, V.S.: Locality-sensitive hashing scheme based on p-stable distributions. In: Proceedings of Annual Symposium on Computational Geometry, SCG 2004, pp. 253–262. ACM (2004)

    Google Scholar 

  7. Elhamifar, E., Vidal, R.: Sparse subspace clustering. In: Proceedings of 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 2790–2797 (2009)

    Google Scholar 

  8. Gao, H., Nie, F., Li, X., Huang, H.: Multi-view subspace clustering. In: Proceedings of 2015 IEEE International Conference on Computer Vision (ICCV), pp. 4238–4246 (2015)

    Google Scholar 

  9. Gong, Y., Lazebnik, S., Gordo, A., Perronnin, F.: Iterative quantization: a procrustean approach to learning binary codes for large-scale image retrieval. IEEE Trans. Pattern Anal. Mach. Intell. 35(12), 2916–2929 (2013)

    Article  Google Scholar 

  10. Hastie, T., Tibshirani, R.: Discriminant adaptive nearest neighbor classification. IEEE Trans. Pattern Anal. Mach. Intell. 18(6), 607–616 (1996)

    Article  Google Scholar 

  11. Indyk, P., Motwani, R.: Approximate nearest neighbors: towards removing the curse of dimensionality. In: Proceedings of Annual ACM Symposium on Theory of Computing, STOC 1998, pp. 604–613. ACM (1998)

    Google Scholar 

  12. Jiang, Q.Y., Li, W.J.: Scalable graph hashing with feature transformation. In: Proceedings of International Conference on Artificial Intelligence, IJCAI 2015, pp. 2248–2254. AAAI Press (2015)

    Google Scholar 

  13. Krizhevsky, A., Hinton, G.: Learning multiple layers of features from tiny images. Technical report, University of Toronto (2009)

    Google Scholar 

  14. Kulis, B., Grauman, K.: Kernelized locality-sensitive hashing. IEEE Trans. Pattern Anal. Mach. Intell. 34(6), 1092–1104 (2012)

    Article  Google Scholar 

  15. Li, C.G., Vidal, R.: Structured sparse subspace clustering: a unified optimization framework. In: Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 277–286 (2015)

    Google Scholar 

  16. Li, X., Hu, D., Nie, F.: Large graph hashing with spectral rotation (2017)

    Google Scholar 

  17. Liong, V.E., Lu, J., Wang, G., Moulin, P., Zhou, J.: Deep hashing for compact binary codes learning. In: Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2475–2483 (2015)

    Google Scholar 

  18. Liu, W., Wang, J., Ji, R., Jiang, Y.G., Chang, S.F.: Supervised hashing with kernels. In: Proceedings of 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 2074–2081 (2012)

    Google Scholar 

  19. Liu, W., Mu, C., Kumar, S., Chang, S.F.: Discrete graph hashing. In: Proceedings of International Conference on Neural Information Processing Systems, NIPS 2014, pp. 3419–3427. MIT Press (2014)

    Google Scholar 

  20. Liu, W., Wang, J., Kumar, S., Chang, S.F.: Hashing with graphs. In: Getoor, L., Scheffer, T. (eds.) Proceedings of International Conference on Machine Learning (ICML-2011), pp. 1–8. ACM (2011)

    Google Scholar 

  21. Luxburg, U.: A tutorial on spectral clustering. Stat. Comput. 17(4), 395–416 (2007)

    Article  MathSciNet  Google Scholar 

  22. Shen, F., Shen, C., Liu, W., Shen, H.T.: Supervised discrete hashing. In: Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 37–45 (2015)

    Google Scholar 

  23. Shen, F., Shen, C., Shi, Q., van den Hengel, A., Tang, Z.: Inductive hashing on manifolds. In: Proceedings of 2013 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1562–1569 (2013)

    Google Scholar 

  24. Shen, F., Zhou, X., Yang, Y., Song, J., Shen, H.T., Tao, D.: A fast optimization method for general binary code learning. IEEE Trans. Image Process. 25(12), 5610–5621 (2016)

    Article  MathSciNet  Google Scholar 

  25. Wang, J., Kumar, S., Chang, S.F.: Semi-supervised hashing for scalable image retrieval. In: Proceedings of 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3424–3431 (2010)

    Google Scholar 

  26. Wang, J., Kumar, S., Chang, S.F.: Semi-supervised hashing for large-scale search. IEEE Trans. Pattern Anal. Mach. Intell. 34(12), 2393–2406 (2012)

    Article  Google Scholar 

  27. Wang, J., Xu, X.S., Guo, S., Cui, L., Wang, X.L.: Linear unsupervised hashing for ANN search in euclidean space. Neurocomputing 171, 283–292 (2016)

    Article  Google Scholar 

  28. Weiss, Y., Torralba, A., Fergus, R.: Spectral hashing. In: Koller, D., Schuurmans, D., Bengio, Y., Bottou, L. (eds.) Proceedings of Advances in Neural Information Processing Systems, vol. 21, pp. 1753–1760. Curran Associates, Inc. (2009)

    Google Scholar 

  29. Xie, L., Shen, J., Zhu, L.: Online cross-modal hashing for web image retrieval. In: Proceedings of AAAI Conference on Artificial Intelligence (AAAI), pp. 294–300 (2016)

    Google Scholar 

  30. Xie, L., Zhu, L., Chen, G.: Unsupervised multi-graph cross-modal hashing for large-scale multimedia retrieval. Multimed. Tools Appl. 75(15), 9185–9204 (2016)

    Article  Google Scholar 

  31. Xie, L., Zhu, L., Pan, P., Lu, Y.: Cross-modal self-taught hashing for large-scale image retrieval. Signal Process. 124, 81–92 (2016)

    Article  Google Scholar 

  32. Xu, H., Wang, J., Li, Z., Zeng, G., Li, S., Yu, N.: Complementary hashing for approximate nearest neighbor search. In: Proceedings of 2011 International Conference on Computer Vision, pp. 1631–1638 (2011)

    Google Scholar 

  33. Zhang, P., Zhang, W., Li, W.J., Guo, M.: Supervised hashing with latent factor models. In: Proceedings of International ACM SIGIR Conference on Research 38; Development in Information Retrieval, SIGIR 2014, pp. 173–182. ACM (2014)

    Google Scholar 

  34. Zhu, L., Shen, J., Xie, L., Cheng, Z.: Unsupervised topic hypergraph hashing for efficient mobile image retrieval. IEEE Trans. Cybern. PP(99), 1–14 (2016)

    Google Scholar 

  35. Zhu, L., She, J., Liu, X., Xie, L., Nie, L.: Learning compact visual representation with canonical views for robust mobile landmark search. In: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, pp. 3959–3965 (2016)

    Google Scholar 

  36. Zhu, L., Shen, J., Xie, L., Cheng, Z.: Unsupervised visual hashing with semantic assistant for content-based image retrieval. IEEE Trans. on Knowl. and Data Eng. 29(2), 472–486 (2017)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia

    Luyao Liu & Lei Zhu

  2. Beijing Etrol Technologies Co., Ltd, Beijing, China

    Zhihui Li

Authors
  1. Luyao Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lei Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhihui Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Luyao Liu , Lei Zhu or Zhihui Li .

Editor information

Editors and Affiliations

  1. University of Queensland, Brisbane, Queensland, Australia

    Zi Huang

  2. Nanyang Technological University, Singapore, Singapore

    Xiaokui Xiao

  3. University of New South Wales, Sydney, New South Wales, Australia

    Xin Cao

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Liu, L., Zhu, L., Li, Z. (2017). Learning Robust Graph Hashing for Efficient Similarity Search. In: Huang, Z., Xiao, X., Cao, X. (eds) Databases Theory and Applications. ADC 2017. Lecture Notes in Computer Science(), vol 10538. Springer, Cham. https://doi.org/10.1007/978-3-319-68155-9_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-68155-9_9

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68154-2

  • Online ISBN: 978-3-319-68155-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation