Abstract
Hashing has been extensively concerned in multimedia research due to its low storage cost and high retrieval efficiency. The purpose of multi-view hashing is to project heterogeneous data from different views in high dimensional space into compact and discrete binary codes in Hamming space and meanwhile maintains the similarity of the original data. In this paper, we propose a Fractional Multi-view Hashing with Semantic Correlation Maximization (FMH-SCM), where the learning objective is to seek the maximum semantic correlation from various views. The proposed method uses labels to learn multi-view hash codes and fractional-order embedding to reduce the negative effect of noise. Moreover, a sequential optimization is used to solve the hash functions for improving the performance of this method. FMH-SCM is compared with related algorithms on two image retrieval datasets. Extensive experiments verify the effectiveness of the proposed method.
Supported by the National Natural Science Foundation of China under Grant Nos. 61402203 and 61703362, the Yangzhou Science Project Foundation under Grant No. YZ2020173. It is also sponsored by Excellent Young Backbone Teacher (Qing Lan) Project and Scientific Innovation Project Fund of Yangzhou University under Grant No. 2017CXJ033.
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
References
Zhu, L., Huang, Z., Li, Z., Xie, L., Shen, H.T.: Exploring auxiliary context: discrete semantic transfer hashing for scalable image retrieval. IEEE Trans. Neural Netw. Learn. Syst. 29(11), 5264–5276 (2018)
Weiss, Y., Torralba, A., Fergus, R.: Spectral hashing. Adv. Neural. Inf. Process. Syst. 282(3), 1753–1760 (2009)
Fang, Y., Li, B., Li, X., Ren, Y.: Unsupervised cross-modal similarity via latent structure discrete hashing factorization. Knowl.-Based Syst. 218, 106857 (2021)
Zhang, D., Wang, F., Si, L.: Composite hashing with multiple information sources. In: Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 225–234 (2011)
Shen, X., et al.: Multiview discrete hashing for scalable multimedia search. ACM Trans. Intell. Syst. Technol. 9(5), 1–21 (2018)
Liu, L., Zhang, Z., Huang, Z.: Flexible discrete multi-view hashing with collective latent feature learning. Neural Process. Lett. 52(3), 1765–1791 (2020)
Liu, W., Wang, J., Kumar, S., Chang, S.F.: Hashing with graphs. In: Proceedings of the 28th International Conference on Machine Learning, pp. 1–8 (2011)
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)
Song, J., Yang, Y., Huang, Z., Shen, H.T., Luo, J.: Effective multiple feature hashing for large-scale near-duplicate video retrieval. IEEE Trans. Multimedia 15(8), 1997–2008 (2013)
Xiang, L., Shen, X., Qin, J., Hao, W.: Discrete multi-graph hashing for large-scale visual search. Neural Process. Lett. 49(3), 1055–1069 (2019)
Lu, X., Zhu, L., Li, J., Zhang, H., Shen, H.T.: Efficient supervised discrete multi-view hashing for large-scale multimedia search. IEEE Trans. Multimedia 22(8), 2048–2060 (2019)
Zhang, D., Li, W.J.: Large-scale supervised multimodal hashing with semantic correlation maximization. In: Proceedings of the AAAI conference on Artificial Intelligence, pp. 2177–2183 (2014)
Yuan, Y.-H., Sun, Q.S.: Fractional-order embedding multiset canonical correlations with applications to multi-feature fusion and recognition. Neurocomputing 122, 229–238 (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Gao, R., Li, Y., Yuan, YH., Qiang, J., Zhu, Y. (2021). Fractional Multi-view Hashing with Semantic Correlation Maximization. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds) Neural Information Processing. ICONIP 2021. Communications in Computer and Information Science, vol 1516. Springer, Cham. https://doi.org/10.1007/978-3-030-92307-5_67
Download citation
DOI: https://doi.org/10.1007/978-3-030-92307-5_67
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-92306-8
Online ISBN: 978-3-030-92307-5
eBook Packages: Computer ScienceComputer Science (R0)