Abstract
Hashing has been widely used to approximate the nearest neighbor search for image retrieval due to its high computation efficiency and low storage requirement. With the development of deep learning, a series of deep supervised methods were proposed for end-to-end binary code learning. However, the similarity between each pair of images is simply defined by whether they belong to the same class or contain common objects, which ignores the heterogeneity within the class. Therefore, those existing methods have not fully addressed the problem and their results are far from satisfactory. Besides, it is difficult and impractical to apply those methods to large-scale datasets. In this paper, we propose a brand new perspective to look into the nature of deep supervised hashing and show that classification models can be directly utilized to generate hashing codes. We also provide a new deep hashing architecture called Deep Supervised Hashing by Classification (DSHC) which takes advantage of both inter-class and intra-class heterogeneity. Experiments on benchmark datasets show that our method outperforms the state-of-the-art supervised hashing methods on accuracy and efficiency.
X. Luo, Y. Guo and Z. Ma—Contribute equally to this work. The work was done when Xiao Luo interned in Damo Academy, Alibaba Group.
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Cao, Z., Long, M., Wang, J., Yu, P.S.: Hashnet: deep learning to hash by continuation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 5608–5617 (2017)
Chen, T., Xu, M., Hui, X., Wu, H., Lin, L.: Learning semantic-specific graph representation for multi-label image recognition. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 522–531 (2019)
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 the ACM International Conference on Image and Video Retrieval, p. 48. ACM (2009)
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 (2012)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Krizhevsky, A., et al.: Learning multiple layers of features from tiny images (2009)
Lai, H., Pan, Y., Liu, Y., Yan, S.: Simultaneous feature learning and hash coding with deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3270–3278 (2015)
Li, Q., Sun, Z., He, R., Tan, T.: Deep supervised discrete hashing. In: Advances in Neural Information Processing Systems, pp. 2482–2491 (2017)
Li, W.J., Wang, S., Kang, W.C.: Feature learning based deep supervised hashing with pairwise labels. In: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, pp. 1711–1717. AAAI Press (2016)
Lin, K., Yang, H.F., Hsiao, J.H., Chen, C.S.: Deep learning of binary hash codes for fast image retrieval. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 27–35 (2015)
Liu, W., Wang, J., Ji, R., Jiang, Y.G., Chang, S.F.: Supervised hashing with kernels. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 2074–2081. IEEE (2012)
Liu, X., He, J., Deng, C., Lang, B.: Collaborative hashing. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2139–2146 (2014)
Liu, X., He, J., Lang, B., Chang, S.F.: Hash bit selection: a unified solution for selection problems in hashing. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1570–1577 (2013)
Luo, X., et al.: A survey on deep hashing methods. arXiv preprint arXiv:2003.03369 (2020)
Luo, X., et al.: Cimon: towards high-quality hash codes. In: IJCAI (2021)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
Wang, J., Yang, Y., Mao, J., Huang, Z., Huang, C., Xu, W.: CNN-RNN: a unified framework for multi-label image classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2285–2294 (2016)
Wang, X., Shi, Y., Kitani, K.M.: Deep supervised hashing with triplet labels. In: Lai, S.-H., Lepetit, V., Nishino, K., Sato, Y. (eds.) ACCV 2016. LNCS, vol. 10111, pp. 70–84. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-54181-5_5
Weiss, Y., Torralba, A., Fergus, R.: Spectral hashing. In: Advances in Neural Information Processing Systems, pp. 1753–1760 (2009)
Wu, L., Fang, Y., Ling, H., Chen, J., Li, P.: Robust mutual learning hashing. In: 2019 IEEE International Conference on Image Processing (ICIP), pp. 2219–2223. IEEE (2019)
Yang, H.F., Lin, K., Chen, C.S.: Supervised learning of semantics-preserving hashing via deep neural networks for large-scale image search. arXiv preprint arXiv:1507.00101 1(2), 3 (2015)
Zhan, J., Mo, Z., Zhu, Y.: Deep self-learning hashing for image retrieval. In: 2020 IEEE International Conference on Image Processing (ICIP), pp. 1556–1560. IEEE (2020)
Zhang, Z., Chen, Y., Saligrama, V.: Efficient training of very deep neural networks for supervised hashing. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1487–1495 (2016)
Zhao, F., Huang, Y., Wang, L., Tan, T.: Deep semantic ranking based hashing for multi-label image retrieval. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1556–1564 (2015)
Zhu, H., Long, M., Wang, J., Cao, Y.: Deep hashing network for efficient similarity retrieval. In: Thirtieth AAAI Conference on Artificial Intelligence (2016)
Acknowledgements
This work was supported by The National Key Research and Development Program of China (No. 2016YFA0502303) and the National Natural Science Foundation of China (No. 31871342).
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Luo, X. et al. (2021). Deep Supervised Hashing by Classification for Image Retrieval. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds) Neural Information Processing. ICONIP 2021. Lecture Notes in Computer Science(), vol 13111. Springer, Cham. https://doi.org/10.1007/978-3-030-92273-3_1
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