Abstract
Learning-based hashing has received increasing research attention due to its promising efficiency for large-scale similarity search. However, most existing manifold-based hashing methods cannot capture the intrinsic structure and discriminative information of image samples. In this paper, we propose a new learning-based hashing method, namely, Sparse Graph Hashing with Spectral Regression (SGHSR), for approximate nearest neighbor search. We first propose a sparse graph model to learn the real-valued codes which can not only preserves the manifold structure of the data, but also adaptively selects sparse and discriminative features. Then, we use a spectral regression to convert the real-valued codes into high-quality binary codes such that the information loss between the original space and the Hamming space can be well minimized. Extensive experimental results on three widely used image databases demonstrate that our SGHSR method outperforms the state-of-the-art unsupervised manifold-based hashing methods.
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References
Andoni, A., Indyk, P.: Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions. Commun. ACM 51(1), 117–122 (2008)
Shuai, C., Wang, X., He, M., Ouyang, X., Yang, J.: A presentation and retrieval hash scheme of images based on principal component analysis. Vis. Comput. 37, 2113–2126 (2021)
Dean, T., Ruzon, M.A., Segal, M., Shlens, J., Vijayanarasimhan, S., Yagnik, J.: Fast, accurate detection of 100,000 object classes on a single machine. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1814–1821 (2013)
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)
Xiao, Y., Zhang, W., Dai, X., Dai, X., Zhang, N.: Robust supervised discrete hashing. Neurocomputing 483, 398–410 (2022)
Qin, J., Fei, L., Zhang, Z., Wen, J., Xu, Y., Zhang, D.: Joint specifics and consistency hash learning for large-scale cross-modal retrieval. IEEE Trans. Image Process. 31, 5343–5358 (2022)
Su, H., Han, M., Liang, J., Liang, J., Yu, S.: Deep supervised hashing with hard example pairs optimization for image retrieval. Vis. Comput. 39, 1–16 (2022)
Liu, J., et al.: Discrete semantic embedding hashing for scalable cross-modal retrieval. In: 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 1461–1467. IEEE (2021)
Qin, J., et al.: Discrete semantic matrix factorization hashing for cross-modal retrieval. In: 2020 25th International Conference on Pattern Recognition (ICPR), pp. 1550–1557. IEEE (2021)
Wang, J., Kumar, S., Chang, S.F.: Semi-supervised hashing for scalable image retrieval. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3424–3431. IEEE (2010)
Hu, H., Wang, K., Lv, C., Wu, J., Yang, Z.: Semi-supervised metric learning-based anchor graph hashing for large-scale image retrieval. IEEE Trans. Image Process. 28(2), 739–754 (2018)
Weiss, Y., Torralba, A., Fergus, R.: Spectral hashing. In: Advances in Neural Information Processing Systems, vol. 21 (2008)
Liu, W., Wang, J., Kumar, S., Chang, S.F.: Hashing with graphs. In: ICML (2011)
Li, X., Hu, D., Nie, F.: Large graph hashing with spectral rotation. In: Thirty-First AAAI Conference on Artificial Intelligence (2017)
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)
Hoang, T., Do, T.T., Le, H., Le-Tan, D.K., Cheung, N.M.: Simultaneous compression and quantization: a joint approach for efficient unsupervised hashing. Comput. Vis. Image Underst. 191, 102852 (2020)
Hu, D., Nie, F., Li, X.: Discrete spectral hashing for efficient similarity retrieval. IEEE Trans. Image Process. 28(3), 1080–1091 (2018)
Jin, S., Yao, H., Zhou, Q., Liu, Y., Huang, J., Hua, X.: Unsupervised discrete hashing with affinity similarity. IEEE Trans. Image Process. 30, 6130–6141 (2021)
Hu, Z., Nie, F., Chang, W., Hao, S., Wang, R., Li, X.: Multi-view spectral clustering via sparse graph learning. Neurocomputing 384, 1–10 (2020)
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)
X, W., et al.: Binary representation via jointly personalized sparse hashing. ACM Trans. Multimed. Comput. Commun. Appl. 18(3s), 1–20 (2022)
Wang, W., Zhang, H., Zhang, Z., Liu, L., Shao, L.: Sparse graph based self-supervised hashing for scalable image retrieval. Inf. Sci. 547, 622–640 (2021)
Wang, W., Shen, Y., Zhang, H., Yao, Y., Liu, L.: Set and rebase: determining the semantic graph connectivity for unsupervised cross-modal hashing. In: Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 853–859 (2021)
Panda, M.R., Kar, S.S., Nanda, A.K., Priyadarshini, R., Panda, S., Bisoy, S.K.: Feedback through emotion extraction using logistic regression and CNN. Vis. Comput. 38(6), 1975–1987 (2022)
Cai, D., He, X., Han, J.: Spectral regression: a unified subspace learning framework for content-based image retrieval. In: Proceedings of the 15th ACM International Conference on Multimedia, pp. 403–412 (2007)
Zhang, L., Yang, M., Feng, X.: Sparse representation or collaborative representation: Which helps face recognition? In: 2011 International Conference on Computer Vision, pp. 471–478. IEEE (2011)
Stewart, G.W.: Matrix Algorithms: Volume II: Eigensystems. In: SIAM (2001)
Cou, C., Guennebaud, G.: Depth from focus using windowed linear least squares regressions. Vis. Comput. 1–10 (2023)
Acknowledgement
This work was supported in part by the National Natural Science Foundation of China under Grants 62176066, 62106052 and 62006059, and in part by the Natural Science Foundation of Guangdong Province under Grant 2023A1515012717.
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He, Z., Qin, J., Fei, L., Zhao, S., Wen, J., Wang, B. (2024). Sparse Graph Hashing with Spectral Regression. In: Sheng, B., Bi, L., Kim, J., Magnenat-Thalmann, N., Thalmann, D. (eds) Advances in Computer Graphics. CGI 2023. Lecture Notes in Computer Science, vol 14498. Springer, Cham. https://doi.org/10.1007/978-3-031-50078-7_4
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