The DOI matches its Crossref record; 45 of the 45 references it cites were found in a registry or an index.
Large-Scale Supervised Multimodal Hashing with Semantic Correlation Maximization
Open accessAbstract
Due to its low storage cost and fast query speed, hashing has been widely adopted for similarity search in multimedia data. In particular, more and more attentions have been payed to multimodal hashing for search in multimedia data with multiple modalities, such as images with tags. Typically, supervised information of semantic labels is also available for the data points in many real applications. Hence, many supervised multimodal hashing~(SMH) methods have been proposed to utilize such semantic labels to further improve the search accuracy. However, the training time complexity of most existing SMH methods is too high, which makes them unscalable to large-scale datasets. In this paper, a novel SMH method, called semantic correlation maximization~(SCM), is proposed to seamlessly integrate semantic labels into the hashing learning procedure for large-scale data modeling. Experimental results on two real-world datasets show that SCM can significantly outperform the state-of-the-art SMH methods, in terms of both accuracy and scalability.
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Blei, D. M., Ng, A. Y., & Jordan, M. I. (2002) · Advances in Neural Information Processing Systems 14 · doi:10.7551/mitpress/1120.003.0082 · Cited by 12,223
RELATIONS BETWEEN TWO SETS OF VARIATES
HOTELLING, H. (1936) · Biometrika · doi:10.1093/biomet/28.3-4.321 · Cited by 5,263
NUS-WIDE: a real-world web image database from National University of Singapore
Chua, T.-S., Tang, J., Hong, R., Li, H., Luo, Z., & Zheng, Y. (2009) · Proceedings of the ACM International Conference on Image and Video Retrieval · doi:10.1145/1646396.1646452 · Cited by 3,025
Weiss, Y., Torralba, A. B., & Fergus, R. (2008) · neural information processing systems · Cited by 2,389
Relations Between Two Sets of Variates
Hotelling, H. (1936) · Biometrika · doi:10.2307/2333955 · Cited by 1,645
Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions
Andoni, A., & Indyk, P. (2008) · Communications of the ACM · doi:10.1145/1327452.1327494 · Cited by 1,437
Supervised hashing with kernels
Liu, W., Wang, J., Ji, R., Jiang, Y.-G., & Chang, S.-F. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2012.6247912 · Cited by 1,351
A new approach to cross-modal multimedia retrieval
Rasiwasia, N., Costa Pereira, J., Coviello, E., Doyle, G., Lanckriet, G. R. G., Levy, R., & Vasconcelos, N. (2010) · Proceedings of the 18th ACM international conference on Multimedia · doi:10.1145/1873951.1873987 · Cited by 1,334
Salakhutdinov, R., & Hinton, G. (2009) · International Journal of Approximate Reasoning · doi:10.1016/j.ijar.2008.11.006 · Cited by 1,246
Iterative quantization: A procrustean approach to learning binary codes
Gong, Y., & Lazebnik, S. (2011) · CVPR 2011 · doi:10.1109/cvpr.2011.5995432 · Cited by 1,038
Cited by
Multimodal Machine Learning: A Survey and Taxonomy
Baltrusaitis, T., Ahuja, C., & Morency, L.-P. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2798607 · Cited by 5,018
Large-Scale Cross-Modality Search via Collective Matrix Factorization Hashing
Ding, G., Guo, Y., Zhou, J., & Gao, Y. (2016) · IEEE Transactions on Image Processing · doi:10.1109/tip.2016.2607421 · Cited by 218
Multi-pathway generative adversarial hashing for unsupervised cross-modal retrieval
Zhang, J., & Peng, Y. (2020) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2019.2922128 · Cited by 103
Metadata
- Authors
- Zhang, D., & Li, W.-J.
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2014-06-21
- Volume
- 28
- Issue
- 1
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W2203543769
- Cited by
- 616
- References
- 45
- FWCI
- 18.74
- Type
- conference paper
- Language
- en
- Open access
- diamond
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- Zhang, D., & Li, W.-J. (2014). Large-Scale Supervised Multimodal Hashing with Semantic Correlation Maximization. Proceedings of the AAAI Conference on Artificial Intelligence. https://doi.org/10.1609/aaai.v28i1.8995
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