The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.
Semantics-preserving hashing for cross-view retrieval
Abstract
With benefits of low storage costs and high query speeds, hashing methods are widely researched for efficiently retrieving large-scale data, which commonly contains multiple views, e.g. a news report with images, videos and texts. In this paper, we study the problem of cross-view retrieval and propose an effective Semantics-Preserving Hashing method, termed SePH. Given semantic affinities of training data as supervised information, SePH transforms them into a probability distribution and approximates it with to-be-learnt hash codes in Hamming space via minimizing the Kullback-Leibler divergence. Then kernel logistic regression with a sampling strategy is utilized to learn the nonlinear projections from features in each view to the learnt hash codes. And for any unseen instance, predicted hash codes and their corresponding output probabilities from observed views are utilized to determine its unified hash code, using a novel probabilistic approach. Extensive experiments conducted on three benchmark datasets well demonstrate the effectiveness and reasonableness of SePH.
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Cites
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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
Self-citationIterative quantization: A procrustean approach to learning binary codes
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Collective Matrix Factorization Hashing for Multimodal Data
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Self-citationInter-media hashing for large-scale retrieval from heterogeneous data sources
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Large-Scale Supervised Multimodal Hashing with Semantic Correlation Maximization
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Cited by
Learning Discriminative Binary Codes for Large-scale Cross-modal Retrieval
Xu, X., Shen, F., Yang, Y., Shen, H. T., & Li, X. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2676345 · Cited by 458
Pairwise Relationship Guided Deep Hashing for Cross-Modal Retrieval
Yang, E., Deng, C., Liu, W., Liu, X., Tao, D., & Gao, X. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.10719 · Cited by 331
Mai, S., Hu, H., & Xing, S. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i01.5347 · Cited by 237
Unsupervised Contrastive Cross-modal Hashing
Hu, P., Zhu, H., Lin, J., Peng, D., Zhao, Y.-P., & Peng, X. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3177356 · Cited by 199
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
- Lin, Z., Ding, G., Hu, M., & Wang, J.
- Venue
- 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2015-06
- Pages
- 3864-3872
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W1922199343
- Cited by
- 542
- References
- 28
- FWCI
- 18.70
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Lin, Z., Ding, G., Hu, M., & Wang, J. (2015). Semantics-preserving hashing for cross-view retrieval. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2015.7299011
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