Abstract
Hashing-based cross-modal retrieval methods have obtained considerable attention due to their efficient retrieval performance and low storage cost. Recently, supervised methods have demonstrated their excellent retrieval accuracy. However, many methods construct a massive similarity matrix by labels and disregard the discrete constraints imposed on the hash codes, which makes it unscalable and results in undesired performance. To overcome these shortcomings, we propose a novel supervised hashing method, named Fast Discrete Matrix Factorization Hashing (FDMFH), which focuses on correlations preservation and the hash codes learning with the discrete constraints. Specifically, FDMFH utilizes matrix factorization to learn a latent semantic space in which relevant data share the same semantic representation. Then, the discriminative hash codes generated by rotating quantization and linear regression preserve the original locality structure of training data. Moreover, an efficient discrete optimization method is used to learn the unified hash codes with a single step. Extensive experiments on two benchmark datasets, MIRFlickr and NUS-WIDE, verify that FDMFH outperforms several state-of-the-art methods.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Wang, J., Liu, W., Kumar, S., Chang, S.: Learning to hash for indexing big data–a survey. Proc. IEEE 104(1), 34–57 (2016)
Peng, Y., Huang, X., Zhao, Y.: An overview of cross-media retrieval: concepts, methodologies, benchmarks, and challenges. IEEE Trans. Circuits Syst. Video Technol. 28(9), 2372–2385 (2018)
Ding, G., Guo, Y., Zhou, J.: Collective matrix factorization hashing for multimodal data. In: CVPR, pp. 2075–2082 (2014)
Zhou, J., Ding, G., Guo, Y.: Latent semantic sparse hashing for cross-modal similarity search. In: SIGIR, pp. 415–424 (2014)
Long, M., Cao, Y., Wang, J., Yu, P.S.: Composite correlation quantization for efficient multimodal retrieval. In: SIGIR, pp. 579–588 (2016)
Su, S., Zhong, Z., Zhang, C.: Deep joint-semantics reconstructing hashing for large-scale unsupervised cross-modal retrieval. In: ICCV, pp. 3027–3035 (2019)
Zhang, D., Li, W.J.: Large-scale supervised multimodal hashing with semantic correlation maximization. In: AAAI, pp. 2177–2183 (2014)
Cao, Y., Liu, B., Long, M., Wang, J.: Cross-modal hamming hashing. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11205, pp. 207–223. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01246-5_13
Tang, J., Wang, K., Shao, L.: Supervised matrix factorization hashing for cross-modal retrieval. IEEE Trans. Image Process. 25(7), 3157–3166 (2016)
Liu, H., Ji, R., Wu, Y., Hua, G.: Supervised matrix factorization for cross-modality hashing. In: IJCAI, pp. 1767–1773 (2016)
Wang, D., Gao, X.B., Wang, X., He, L.: Label consistent matrix factorization hashing for large-scale cross-modal similarity search. IEEE Trans. Pattern Anal. Mach. Intell. 41(10), 2466–2479 (2019)
Zhao, H., Wang, S., She, X., Su, C.: Supervised matrix factorization hashing with quantitative loss for image-text search. IEEE Access 8, 102051–102064 (2020)
Zhang, X., Lai, H., Feng, J.: Attention-aware deep adversarial hashing for cross-modal retrieval. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11219, pp. 614–629. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01267-0_36
Lin, Z., Ding, G., Hu, M., Wang, J.: Semantics-preserving hashing for cross-view retrieval. In: CVPR, pp. 3864–3872 (2015)
Xu, X., Shen, F., Yang, Y., Shen, H.T., Li, X.: Learning discriminative binary codes for large-scale cross-modal retrieval. IEEE Trans. Image Process. 26(5), 2494–2507 (2017)
Shen, F., Shen, C., Liu, W., Shen, H.T.: Supervised discrete hashing. In: CVPR, pp. 37–45 (2015)
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 (2013)
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)
Watson, G.A.: Characterization of the subdifferential of some matrix norms. Linear Alg. Appl. 170, 33–45 (1992)
Hoerl, A.E., Kennard, R.W.: Ridge regression: applications to nonorthogonal problems. Technometrics 12(1), 69–82 (1970)
Huiskes, M.J., Lew, M.S.: The MIR flickr retrieval evaluation. In: ICMR, pp. 39–43 (2008)
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: CIVR, pp. 48–56 (2009)
Baeza-Yates, R., Ribeiro-Neto, B.: Modern Information Retrieval, vol. 463. ACM Press, New York (1999)
Acknowledgments
This work was supported by National Key R&D Program of China (2018YFC0831800).
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
Zhao, H., She, X., Wang, S., Ma, K. (2021). Fast Discrete Matrix Factorization Hashing for Large-Scale Cross-Modal Retrieval. In: Lokoč, J., et al. MultiMedia Modeling. MMM 2021. Lecture Notes in Computer Science(), vol 12572. Springer, Cham. https://doi.org/10.1007/978-3-030-67832-6_3
Download citation
DOI: https://doi.org/10.1007/978-3-030-67832-6_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-67831-9
Online ISBN: 978-3-030-67832-6
eBook Packages: Computer ScienceComputer Science (R0)