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World Wide Web

, Volume 22, Issue 2, pp 477–497 | Cite as

Class consistent hashing for fast Web data searching

  • Xin Luo
  • Ye Wu
  • Wan-Jin Yu
  • Xin-Shun XuEmail author
Article
  • 267 Downloads
Part of the following topical collections:
  1. Special Issue on Deep vs. Shallow: Learning for Emerging Web-scale Data Computing and Applications

Abstract

Hashing based ANN search has drawn lots of attention due to its low storage and time cost. Supervised hashing methods can leverage label information to generate compact and accurate hash codes and have achieved promising results. However, when dealing with the learning problem, most of existing supervised hashing methods are time-consuming and unscalable. To overcome these limitations, we propose a novel supervised hashing method named Class Consistent Hashing (CCH). In particular, CCH avoids using instance pairwise semantic similarity matrix which is widely used in existing methods. Instead, it uses class-pairwise semantic similarity whose size is far less than the former one, and generates hash codes for every class by optimizing the least-squares style objective function. Then, instances in the same class share the same class hash codes. Finally, we adopt a two-step hashing design strategy to learn the hash functions for out-of-sample instances. Experimental results on several widely used datasets illustrate that CCH can outperform several state-of-the-art shallow methods with the fastest training speed among supervised hashing methods.

Keywords

Hashing Similarity preserving Large-scale data Web data search Approximate nearest neighbor search 

Notes

Acknowledgments

This work was partially supported by National Natural Science Foundation of China (61573212), Key Research and Development Program of Shandong Province (2016GGX101044).

References

  1. 1.
    Datar, M., Immorlica, N., Indyk, P., Mirrokni, V.S.: Locality-sensitive hashing scheme based on p-stable distributions. In: Proceedings of the 20th Annual Symposium on Computational Geometry (SoCG 2004), pp. 253–262 (2004)Google Scholar
  2. 2.
    Ding, G., Guo, Y., Zhou, J.: Collective matrix factorization hashing for multimodal data. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2014), pp. 2083–2090 (2014)Google Scholar
  3. 3.
    Gionis, A., Indyk, P., Motwani, R.: Similarity search in high dimensions via hashing. In: Proceedings of 25th International Conference on Very Large Data Bases (VLDB 1999), pp. 518–529 (1999)Google Scholar
  4. 4.
    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)CrossRefGoogle Scholar
  5. 5.
    Hao, Y., Mu, T., Goulermas, J.Y., Jiang, J., Hong, R., Wang, M.: Unsupervised t-distributed video hashing and its deep hashing extension. IEEE Trans. Image Process. 26(11), 5531–5544 (2017)MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    He, X., Zhang, H., Kan, Y.M., Chua, T.S.: Fast matrix factorization for online recommendation with implicit feedback. In: Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2016), pp. 549–558 (2016)Google Scholar
  7. 7.
    Herranz, L., Jiang, S., Li, X.: Scene recognition with CNNs: objects, scales and dataset bias. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2016), pp. 571–579 (2016)Google Scholar
  8. 8.
    Huang, L.K., Pan, S.J.: Class-wise supervised hashing with label embedding and active bits. In: Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI 2016), pp. 1585–1591 (2016)Google Scholar
  9. 9.
    Jiang, Q.Y., Li, W.J.: Scalable graph hashing with feature transformation. In: Proceedings of the 24th International Joint Conference on Artificial Intelligence (IJCAI 2015), pp. 2248–2254 (2015)Google Scholar
  10. 10.
    Jiang, Q.Y., Li, W.J.: Deep cross-modal hashing. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017), pp. 3270–3278 (2017)Google Scholar
  11. 11.
    Kang, W.C., Li, W.J., Zhou, Z.H.: Column sampling based discrete supervised hashing. In: Proceedings of the 30th Conference on Artificial Intelligence (AAAI 2016), pp. 1230–1236 (2016)Google Scholar
  12. 12.
    Kong, W., Li, W.J.: Isotropic hashing. In: Proceedings of the 25th Annual Conference on Neural Information Processing Systems (NIPS 2012), pp. 1655–1663 (2012)Google Scholar
  13. 13.
    Krizhevsky, A.: Learning multiple layers of features from tiny images. Master’s thesis, University of Toronto, Toronto (2009)Google Scholar
  14. 14.
    Kulis, B., Grauman, K.: Kernelized locality-sensitive hashing for scalable image search. In: Proceedings of the 12th International Conference on Computer Vision (ICCV 2009), pp. 2130–2137 (2009)Google Scholar
  15. 15.
    Kulis, B., Jain, P., Grauman, K.: Fast similarity search for learned metrics. IEEE Trans. Pattern Anal. Mach. Intell. 31(12), 2143–2157 (2009)CrossRefGoogle Scholar
  16. 16.
    LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)CrossRefGoogle Scholar
  17. 17.
    Lin, G., Shen, C., Suter, D., Hengel, A.V.D.: A general two-step approach to learning-based hashing. In: Proceedings of the 16th International Conference on Computer Vision (ICCV 2013), pp. 2552–2559 (2013)Google Scholar
  18. 18.
    Lin, G., Shen, C., Shi, Q., Hengel, A.V.D., Suter, D.: Fast supervised hashing with decision trees for high-dimensional data. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2014), pp. 1971–1978 (2014)Google Scholar
  19. 19.
    Lin, Z., Ding, G., Hu, M., Wang, J.: Semantics-preserving hashing for cross-view retrieval. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2015), pp. 3864–3872 (2015)Google Scholar
  20. 20.
    Liu, W., Wang, J., Kumar, S., Chang, S.F.: Hashing with graphs. In: Proceedings of the 28th International conference on machine learning (ICML 2011), pp. 1–8 (2011)Google Scholar
  21. 21.
    Liu, W., Wang, J., Ji, R., Jiang, Y.G., Chang, S.F.: Supervised hashing with kernels. In: Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (CVPR 2012), pp. 2074–2081 (2012)Google Scholar
  22. 22.
    Liu, X., He, J., Lang, B.: Multiple feature kernel hashing for large-scale visual search. Pattern Recog. 47(2), 748–757 (2014)CrossRefzbMATHGoogle Scholar
  23. 23.
    Liu, X., Mu, Y., Zhang, D., Lang, B., Li, X.: Large-scale unsupervised hashing with shared structure learning. IEEE Trans. Image Cybern. 45(9), 1811–1822 (2015)CrossRefGoogle Scholar
  24. 24.
    Liu, X., Deng, C., Lang, B., Tao, D., Li, X.: Query-adaptive reciprocal hash tables for nearest neighbor search. IEEE Trans. Image Process. 25(2), 907–919 (2016)MathSciNetCrossRefzbMATHGoogle Scholar
  25. 25.
    Liu, X., Huang, L., Deng, C., Lang, B., Tao, D.: Query-adaptive hash code ranking for large-scale multi-view visual search. IEEE Trans. Image Process. 25 (10), 4514–4524 (2016)MathSciNetCrossRefzbMATHGoogle Scholar
  26. 26.
    Liu, X., Du, B., Deng, C., Liu, M., Lang, B.: Structure sensitive hashing with adaptive product quantization. IEEE Trans. Cybern. 46(10), 2252–2264 (2016)CrossRefGoogle Scholar
  27. 27.
    Liu, X., He, J., Chang, S.F.: Hash bit selection for nearest neighbor search. IEEE Trans. Image Process. 26(11), 5367–5380 (2017)MathSciNetCrossRefzbMATHGoogle Scholar
  28. 28.
    Liu, X., Li, Z., Deng, C., Tao, D.: Distributed adaptive binary quantization for fast nearest neighbor search. IEEE Trans. Image Process. 26(11), 5324–5336 (2017)MathSciNetCrossRefzbMATHGoogle Scholar
  29. 29.
    Nie, L., Wang, M., Zha, Z.J., Chua, T.S.: Oracle in image search: A content-based approach to performance prediction. ACM Trans. Inf. Syst. 30(2), 13:1–13:23 (2012)CrossRefGoogle Scholar
  30. 30.
    Norouzi, M., Fleet, D.J.: Minimal loss hashing for compact binary codes. In: Proceedings of the 28th International Conference on Machine Learning (ICML 2011), pp. 353–360 (2011)Google Scholar
  31. 31.
    Oliva, A., Torralba, A.: Modeling the shape of the scene: A holistic representation of the spatial envelope. Int. J. Comput. Vis. 42(3), 145–175 (2001)CrossRefzbMATHGoogle Scholar
  32. 32.
    Raginsky, M., Lazebnik, S.: Locality-sensitive binary codes from shift-invariant kernels (2009)Google Scholar
  33. 33.
    Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M.S., Berg, A.C., Fei-Fei, L.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015)MathSciNetCrossRefGoogle Scholar
  34. 34.
    Shen, F., Shen, C., Shi, Q., Hengel, A.V.D., Tang, Z.: Inductive hashing on manifolds. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2013), pp. 1562–1569 (2013)Google Scholar
  35. 35.
    Shen, F., Shen, C., Liu, W., Shen, H.T.: Supervised discrete hashing. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2015), pp. 37–45 (2015)Google Scholar
  36. 36.
    Song, J., Yang, Y., Huang, Z., Shen, H.T., Luo, J.: Effective multiple feature hashing for large-scale near-duplicate Video Retrieval. IEEE Trans. Multimed. 15(8), 1997–2008 (2013)CrossRefGoogle Scholar
  37. 37.
    Song, J., Yang, Y., Li, X., Huang, Z., Yang, Y.: Robust hashing with local models for approximate similarity search. IEEE Trans. Cybern. 44(7), 1225–1236 (2014)CrossRefGoogle Scholar
  38. 38.
    Song, J., Gao, L., Yan, Y., Zhang, D., Sebe, N.: Supervised hashing with pseudo labels for scalable multimedia retrieval. In: Proceedings of the 23rd ACM international conference on Multimedia (MM 2015), pp. 827–830 (2015)Google Scholar
  39. 39.
    Song, J., Gao, L., Zou, F., Yan, Y., Sebe, N.: Deep and fast: Deep learning hashing with semi-supervised graph construction. Image Vision Comput. 55, 101–108 (2016)CrossRefGoogle Scholar
  40. 40.
    Song, X., Jiang, S., Gao, Y., Herranz, L.: Multi-scale multi-feature context modeling for scene recognition in the semantic manifold. IEEE Trans. Image Process. 26(6), 2721–2735 (2017)MathSciNetCrossRefzbMATHGoogle Scholar
  41. 41.
    Song, J.: Binary generative adversarial networks for image retrieval. In: Proceedings of the 32nd Conference on Artificial Intelligence (AAAI 2018) (2018)Google Scholar
  42. 42.
    Song, J., Gao, L., Liu, L., Zhu, X., Sebe, N.: Quantization-based hashing: a general framework for scalable image and video retrieval. Pattern Recog. 75, 175–187 (2018)CrossRefGoogle Scholar
  43. 43.
    Song, J., He, T., Gao, L., Xu, X., Shen, H.T.: Deep region hashing for efficient large-scale instance search from images. In: Proceedings of the 32nd Conference on Artificial Intelligence (AAAI 2018) (2018)Google Scholar
  44. 44.
    Tang, J., Li, Z., Wang, M., Zhao, R.: Neighborhood discriminant hashing for large-scale image retrieval. IEEE Trans. Image Process. 24(9), 2827–2840 (2015)MathSciNetCrossRefzbMATHGoogle Scholar
  45. 45.
    Wang, J., Kumar, S., Chang, S.F.: Semi-supervised hashing for scalable image retrieval. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2010), pp. 3424–3431 (2010)Google Scholar
  46. 46.
    Wang, S., Jiang, S.: INSTRE: a new benchmark for instance-level object retrieval and recognition. ACM Trans. Multimedia Comput. Commun. Appl. 11(3), 37:1–37:21 (2015)CrossRefGoogle Scholar
  47. 47.
    Wang, J., Xu, X.S., Guo, S., Cui, L., Wang, X.: Linear unsupervised hashing for ANN search in Euclidean space. Neurocomputing 171, 283–292 (2016)CrossRefGoogle Scholar
  48. 48.
    Weiss, Y., Torralba, A., Fergus, R.: Spectral hashing. In: Proceedings of the 21st Annual Conference on Neural Information Processing Systems (NIPS 2008), pp. 1753–1760 (2008)Google Scholar
  49. 49.
    Xia, R., Pan, Y., Lai, H., Liu, C., Yan, S.: Supervised hashing for image retrieval via image representation learning. In: Proceedings of the 28th Conference on Artificial Intelligence (AAAI 2014), pp. 2156–2162 (2014)Google Scholar
  50. 50.
    Xu, X.S.: Dictionary learning based hashing for cross-modal retrieval. In: Proceedings of the 24th ACM International Conference on Multimedia (MM 2016), pp. 177–181 (2016)Google Scholar
  51. 51.
    Yan, T.K., Xu, X.S., Guo, S., Huang, Z., Wang, X.: Supervised robust discrete multimodal hashing for cross-media retrieval. In: Proceedings of the 25th ACM International Conference on Information and Knowledge Management (CIKM 2016), pp. 1271–1280 (2016)Google Scholar
  52. 52.
    Yang, Y., Zha, Z.J., Gao, Y., Zhu, X., Chua, T.S.: Exploiting Web images for semantic video indexing via robust sample-specific loss. IEEE Trans. Multimed. 16 (6), 1677–1689 (2014)CrossRefGoogle Scholar
  53. 53.
    Yang, Y., Ma, Z., Yang, Y., Nie, F., Shen, H.T.: Multitask spectral clustering by exploring intertask correlation. IEEE Trans. Cybern. 45(5), 1069–1080 (2015)CrossRefGoogle Scholar
  54. 54.
    Zhang, D., Wang, J., Cai, D., Lu, J.: Self-taught hashing for fast similarity search. In: Proceedings of the 33rd international ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2010), pp. 18–25 (2010)Google Scholar
  55. 55.
    Zhang, D., Li, W.J.: Large-scale supervised multimodal hashing with semantic correlation maximization. In: Proceedings of the 28th Conference on Artificial Intelligence (AAAI 2014), pp. 2177–2183 (2014)Google Scholar
  56. 56.
    Zhang, P., Zhang, W., Li, W.J., Guo, M.: Supervised hashing with latent factor models. In: Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2014), pp. 173–182 (2014)Google Scholar
  57. 57.
    Zhou, K., Liu, Y., Song, J., Yan, L., Zou, F., Shen, F.: Deep self-taught hashing for image retrieval. In: Proceedings of the 23rd ACM International Conference on Multimedia (MM 2015), pp. 1215–1218 (2015)Google Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Shandong UniversityJinanChina

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