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Unsupervised Deep Hashing via Adaptive Clustering

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Web and Big Data (APWeb-WAIM 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12859))

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Abstract

Similarity-preserved hashing has become a popular technique for large-scale image retrieval because of its low storage cost and high search efficiency. Unsupervised hashing has high practical value because it learns hash functions without any annotated label. Previous unsupervised hashing methods usually obtain the semantic similarities between data points by taking use of deep features extracted from pre-trained CNN networks. The semantic structure learned from fixed embeddings are often not the optimal, leading to sub-optimal retrieval performance. To tackle the problem, in this paper, we propose a Deep Clustering based Unsupervised Hashing architecture, called DCUH. The proposed model can simultaneously learn the intrinsic semantic relationships and hash codes. Specifically, DCUH first clusters the deep features to generate the pseudo classification labels. Then, DCUH is trained by both the classification loss and the discriminative loss. Concretely, the pseudo class label is used as the supervision for classification. The learned hash code should be invariant under different data augmentations with the local semantic structure preserved. Finally, DCUH is designed to update the cluster assignments and train the deep hashing network iteratively. Extensive experiments demonstrate that the proposed model outperforms the state-of-the-art unsupervised hashing methods.

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Notes

  1. 1.

    https://www.cs.toronto.edu/~kriz/cifar.html.

  2. 2.

    http://press.liacs.nl/mirflick.

  3. 3.

    http://mscoco.org.

  4. 4.

    https://pytorch.org/docs/stable/index.html.

References

  1. Bentley, J.L.: Multidimensional binary search trees used for associative searching. Commun. ACM 18(9), 509–517 (1975)

    Article  Google Scholar 

  2. Cao, Z., Long, M., Wang, J., Yu, P.S.: HashNet: deep learning to hash by continuation. In: CVPR, pp. 5608–5617 (2017)

    Google Scholar 

  3. Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. arXiv preprint arXiv:2002.05709 (2020)

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

    Google Scholar 

  5. Do, T.-T., Doan, A.-D., Cheung, N.-M.: Learning to hash with binary deep neural network. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9909, pp. 219–234. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46454-1_14

    Chapter  Google Scholar 

  6. Gattupalli, V., Zhuo, Y., Li, B.: Weakly supervised deep image hashing through tag embeddings. In: CVPR, pp. 10375–10384 (2019)

    Google Scholar 

  7. Gionis, A., Indyk, P., Motwani, R.: Similarity search in high dimensions via hashing. In: VLDB 1999, pp. 518–529 (1999)

    Google Scholar 

  8. Gong, Y., Lazebnik, S.: Iterative quantization: a procrustean approach to learning binary codes. In: CVPR, pp. 817–824 (2011)

    Google Scholar 

  9. Guttman, A.: R-trees: a dynamic index structure for spatial searching. In: SIGMOD 1984, pp. 47–57. ACM Press (1984)

    Google Scholar 

  10. He, K., Wen, F., Sun, J.: K-means hashing: an affinity-preserving quantization method for learning binary compact codes. In: CVPR, pp. 2938–2945 (2013)

    Google Scholar 

  11. Heo, J.P., Lee, Y., He, J., Chang, S.F., Yoon, S.E.: Spherical hashing. In: CVPR, pp. 2957–2964 (2012)

    Google Scholar 

  12. Hu, Q., Wu, J., Cheng, J., Wu, L., Lu, H.: Pseudo label based unsupervised deep discriminative hashing for image retrieval. In: Proceedings of the 2017 ACM on Multimedia Conference, MM, pp. 1584–1590 (2017)

    Google Scholar 

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

    Google Scholar 

  14. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: NIPS, pp. 1106–1114 (2012)

    Google Scholar 

  15. Kulis, B., Darrell, T.: Learning to hash with binary reconstructive embeddings. In: NIPS, pp. 1042–1050 (2009)

    Google Scholar 

  16. Lai, H., Pan, Y., Liu, Y., Yan, S.: Simultaneous feature learning and hash coding with deep neural networks. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR, pp. 3270–3278 (2015)

    Google Scholar 

  17. Li, Q., Sun, Z., He, R., Tan, T.: Deep supervised discrete hashing. In: NIPS, pp. 2482–2491 (2017)

    Google Scholar 

  18. Li, W., Wang, S., Kang, W.: Feature learning based deep supervised hashing with pairwise labels. In: IJCAI, pp. 1711–1717 (2016)

    Google Scholar 

  19. Li, Y., van Gemert, J.: Deep unsupervised image hashing by maximizing bit entropy. CoRR arxiv:abs/2012.12334 (2020)

  20. Lin, K., Lu, J., Chen, C., Zhou, J.: Learning compact binary descriptors with unsupervised deep neural networks. In: CVPR, pp. 1183–1192 (2016)

    Google Scholar 

  21. Lin, K., Lu, J., Chen, C., Zhou, J., Sun, M.: Unsupervised deep learning of compact binary descriptors. IEEE Trans. Pattern Anal. Mach. Intell. 41(6), 1501–1514 (2019)

    Article  Google Scholar 

  22. Lin, K., Yang, H., Hsiao, J., Chen, C.: Deep learning of binary hash codes for fast image retrieval. In: CVPR, pp. 27–35 (2015)

    Google Scholar 

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

    Google Scholar 

  24. Liu, W., Mu, C., Kumar, S., Chang, S.: Discrete graph hashing. In: NIPS, pp. 3419–3427 (2014)

    Google Scholar 

  25. Liu, W., Wang, J., Ji, R., Jiang, Y., Chang, S.: Supervised hashing with kernels. In: CVPR, pp. 2074–2081 (2012)

    Google Scholar 

  26. Liu, W., Wang, J., Kumar, S., Chang, S.: Hashing with graphs. In: Proceedings of the 28th International Conference on Machine Learning, ICML, pp. 1–8. Omnipress (2011)

    Google Scholar 

  27. Van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9(Nov), 2579–2605 (2008)

    MATH  Google Scholar 

  28. Norouzi, M., Fleet, D.J.: Minimal loss hashing for compact binary codes. In: ICML, pp. 353–360 (2011)

    Google Scholar 

  29. Shen, F., Shen, C., Liu, W., Shen, H.T.: Supervised discrete hashing. In: CVPR, pp. 37–45. IEEE Computer Society (2015)

    Google Scholar 

  30. Shen, F., Xu, Y., Liu, L., Yang, Y., Huang, Z., Shen, H.T.: Unsupervised deep hashing with similarity-adaptive and discrete optimization. IEEE Trans. Pattern Anal. Mach. Intell. 40, 3034–3044 (2018)

    Article  Google Scholar 

  31. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)

  32. Su, S., Zhang, C., Han, K., Tian, Y.: Greedy hash: towards fast optimization for accurate hash coding in CNN. In: NIPS, pp. 806–815 (2018)

    Google Scholar 

  33. Weiss, Y., Torralba, A., Fergus, R.: Spectral hashing. In: NIPS, pp. 1753–1760. Curran Associates, Inc. (2008)

    Google Scholar 

  34. Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised feature learning via non-parametric instance discrimination. In: CVPR, pp. 3733–3742. IEEE Computer Society (2018)

    Google Scholar 

  35. Xia, R., Pan, Y., Lai, H., Liu, C., Yan, S.: Supervised hashing for image retrieval via image representation learning. In: Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, pp. 2156–2162 (2014)

    Google Scholar 

  36. Yang, E., Deng, C., Liu, T., Liu, W., Tao, D.: Semantic structure-based unsupervised deep hashing. In: Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI, pp. 1064–1070 (2018)

    Google Scholar 

  37. Yang, E., Liu, T., Deng, C., Liu, W., Tao, D.: DistillHash: unsupervised deep hashing by distilling data pairs. In: CVPR, pp. 2946–2955 (2019)

    Google Scholar 

  38. Ye, M., Zhang, X., Yuen, P.C., Chang, S.: Unsupervised embedding learning via invariant and spreading instance feature. In: CVPR, pp. 6210–6219 (2019)

    Google Scholar 

  39. Zhang, P., Zhang, W., Li, W., Guo, M.: Supervised hashing with latent factor models. In: SIGIR, pp. 173–182 (2014)

    Google Scholar 

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Acknowledgement

The work is supported by National Key R&D Plan (No.2018 YFB1005100), National Natural Science Foundation of China (No. 61751201, 61602197 and 61772076), Natural Science Fund of Beijing (No. Z181100008918002) and the funds of Beijing Advanced Innovation Center for Language Resources (No. TYZ19005).

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Authors and Affiliations

  1. Beijing Institute of Technology, Beijing, 100081, China

    Shuying Yu, Xian-Ling Mao & Heyan Huang

  2. Huazhong University of Science and Technology, Wuhan, 430073, China

    Wei Wei

Authors
  1. Shuying Yu
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  2. Xian-Ling Mao
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  3. Wei Wei
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  4. Heyan Huang
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Corresponding author

Correspondence to Xian-Ling Mao .

Editor information

Editors and Affiliations

  1. University of Macau, Macau, China

    Leong Hou U

  2. University of Caen Normandie, Caen, France

    Marc Spaniol

  3. Osaka University, Osaka, Japan

    Yasushi Sakurai

  4. South China University of Technology, Guangzhou, China

    Junying Chen

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Yu, S., Mao, XL., Wei, W., Huang, H. (2021). Unsupervised Deep Hashing via Adaptive Clustering. In: U, L.H., Spaniol, M., Sakurai, Y., Chen, J. (eds) Web and Big Data. APWeb-WAIM 2021. Lecture Notes in Computer Science(), vol 12859. Springer, Cham. https://doi.org/10.1007/978-3-030-85899-5_1

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  • DOI: https://doi.org/10.1007/978-3-030-85899-5_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-85898-8

  • Online ISBN: 978-3-030-85899-5

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