Abstract
Hashing technology, one of the most efficient approximate nearest neighbor searching methods due to its fast query speed and low storage cost, has been widely used in image retrieval. Recently, unsupervised deep hashing methods have attracted more and more attention due to the lack of labels in real applications. Most unsupervised hashing methods usually construct a similarity matrix with the features extracted from the images, and then guide the hash code learning with this similarity matrix. However, in unsupervised scenario, such similarity matrix may be unreliable due to the affect of noise and irrelevant objects in images. In this paper, we propose a novel unsupervised deep hashing method called Deep Structured Hashing (DSH). In the new method, we first learn both continuous and binary structured similarity matrices with explicit cluster structure to better preserve the semantic structure, where the binary one preserves the coarse-grained semantic structure while the continuous one preserves the fine-grained semantic structure. And then jointly optimize three kinds of losses to learn high quality hash codes. Extensive experiments on three benchmark datasets show the superior retrieval performance of our proposed method.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Andoni, A., Indyk, P.: Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions. In: 2006 47th Annual IEEE Symposium on Foundations of Computer Science (FOCS 2006), pp. 459–468. IEEE (2006)
Bentley, J.L.: Multidimensional binary search trees used for associative searching. Commun. ACM 18(9), 509–517 (1975)
Chen, Y., Lai, Z., Ding, Y., Lin, K., Wong, W.K.: Deep supervised hashing with anchor graph. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 9796–9804 (2019)
Dai, B., Guo, R., Kumar, S., He, N., Song, L.: Stochastic generative hashing. In: Proceedings of the 34th International Conference on Machine Learning-Volume 70, pp. 913–922. JMLR. org (2017)
Gionis, A., Indyk, P., Motwani, R., et al.: Similarity search in high dimensions via hashing. In: Vldb, vol. 99, pp. 518–529 (1999)
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)
Gui, J., Liu, T., Sun, Z., Tao, D., Tan, T.: Fast supervised discrete hashing. IEEE Trans. Pattern Anal. Mach. Intell. 40(2), 490–496 (2017)
Indyk, P., Motwani, R.: Approximate nearest neighbors: towards removing the curse of dimensionality. In: Proceedings of the 30th Annual ACM symposium on Theory of computing, pp. 604–613 (1998)
Jiang, Q.Y., Li, W.J.: Asymmetric deep supervised hashing. In: 32nd AAAI Conference on Artificial Intelligence (2018)
Kang, W.C., Li, W.J., Zhou, Z.H.: Column sampling based discrete supervised hashing. In: 30th AAAI Conference on Artificial Intelligence (2016)
Kulis, B., Grauman, K.: Kernelized locality-sensitive hashing for scalable image search. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 2130–2137. IEEE (2009)
Lai, Z., Chen, Y., Wu, J., Wong, W.K., Shen, F.: Jointly sparse hashing for image retrieval. IEEE Trans. Image Process. 27(12), 6147–6158 (2018)
Li, X., Hu, D., Nie, F.: Large graph hashing with spectral rotation. In: 31st AAAI Conference on Artificial Intelligence (2017)
Lin, K., Lu, J., Chen, C.S., Zhou, J.: Learning compact binary descriptors with unsupervised deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1183–1192 (2016)
Liu, W., Wang, J., Kumar, S., Chang, S.F.: Hashing with graphs. In: Proceedings ICML, pp. 1–8 (2011)
Luo, X., Nie, L., He, X., Wu, Y., Chen, Z.D., Xu, X.S.: Fast scalable supervised hashing. In: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, pp. 735–744. ACM (2018)
Luo, Y., Tao, D., Xu, C., Li, D., Xu, C.: Vector-valued multi-view semi-supervised learning for multi-label image classification. In: Proceedings of the 27th AAAI Conference on Artificial Intelligence, pp. 647–653. AAAI 2013 (2013)
Maaten, L.V.D., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9(Nov), 2579–2605 (2008)
Nie, F., Wang, X., Huang, H.: Clustering and projected clustering with adaptive neighbors. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 977–986. ACM (2014)
Shen, F., Shen, C., Liu, W., Tao Shen, H.: Supervised discrete hashing. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 37–45 (2015)
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(12), 3034–3044 (2018)
Song, J., He, T., Gao, L., Xu, X., Hanjalic, A., Shen, H.T.: Binary generative adversarial networks for image retrieval. In: 32nd AAAI Conference on Artificial Intelligence (2018)
Su, S., Zhang, C., Han, K., Tian, Y.: Greedy hash: towards fast optimization for accurate hash coding in cnn. In: Advances in Neural Information Processing Systems, pp. 798–807 (2018)
Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)
Wang, J., Zhang, T., Song, J., Sebe, N., Shen, H.T.: A survey on learning to hash. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 769–790 (2017)
Weiss, Y., Torralba, A., Fergus, R.: Spectral hashing. In: Advances in Neural Information Processing Systems, pp. 1753–1760 (2009)
Xia, Y., He, K., Kohli, P., Sun, J.: Sparse projections for high-dimensional binary codes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3332–3339 (2015)
Yang, E., Deng, C., Liu, T., Liu, W., Tao, D.: Semantic structure-based unsupervised deep hashing. In: IJCAI, pp. 1064–1070 (2018)
Zhang, Z., et al.: Scalable supervised asymmetric hashing with semantic and latent factor embedding. IEEE Trans. Image Process. 28(10), 4803–4818 (2019)
Zhu, H., Long, M., Wang, J., Cao, Y.: Deep hashing network for efficient similarity retrieval. In: 30th AAAI Conference on Artificial Intelligence (2016)
Acknowledgments
This research was supported by Major Project of the New Generation of Artificial Intelligence(No. 2018AAA0102900), NSFC under Grant no. 61773268, Natural Science Foundation of SZU (no. 000346) and the Shenzhen Research Foundation for Basic Research, China (Nos. JCYJ20180305124149387).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Pang, X., Chen, X., Yang, S., Nie, F. (2020). Unsupervised Deep Hashing with Structured Similarity Learning. In: Wang, X., Zhang, R., Lee, YK., Sun, L., Moon, YS. (eds) Web and Big Data. APWeb-WAIM 2020. Lecture Notes in Computer Science(), vol 12318. Springer, Cham. https://doi.org/10.1007/978-3-030-60290-1_38
Download citation
DOI: https://doi.org/10.1007/978-3-030-60290-1_38
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-60289-5
Online ISBN: 978-3-030-60290-1
eBook Packages: Computer ScienceComputer Science (R0)