The DOI matches its Crossref record; 42 of the 42 references it cites were found in a registry or an index.
Learning Discriminative Binary Codes for Large-scale Cross-modal Retrieval
Open accessAbstract
Hashing based methods have attracted considerable attention for efficient cross-modal retrieval on large-scale multimedia data. The core problem of cross-modal hashing is how to learn compact binary codes that construct the underlying correlations between heterogeneous features from different modalities. A majority of recent approaches aim at learning hash functions to preserve the pairwise similarities defined by given class labels. However, these methods fail to explicitly explore the discriminative property of class labels during hash function learning. In addition, they usually discard the discrete constraints imposed on the to-be-learned binary codes, and compromise to solve a relaxed problem with quantization to obtain the approximate binary solution. Therefore, the binary codes generated by these methods are suboptimal and less discriminative to different classes. To overcome these drawbacks, we propose a novel cross-modal hashing method, termed discrete cross-modal hashing (DCH), which directly learns discriminative binary codes while retaining the discrete constraints. Specifically, DCH learns modality-specific hash functions for generating unified binary codes, and these binary codes are viewed as representative features for discriminative classification with class labels. An effective discrete optimization algorithm is developed for DCH to jointly learn the modality-specific hash function and the unified binary codes. Extensive experiments on three benchmark data sets highlight the superiority of DCH under various cross-modal scenarios and show its state-of-the-art performance.
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Cited by
Adversarial Cross-Modal Retrieval
Wang, B., Yang, Y., Xu, X., Hanjalic, A., & Shen, H. T. (2017) · Proceedings of the 25th ACM international conference on Multimedia · doi:10.1145/3123266.3123326 · Cited by 779
Self-citationDeep Fuzzy Hashing Network for Efficient Image Retrieval
Lu, H., Zhang, M., Xu, X., Li, Y., & Shen, H. T. (2021) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2020.2984991 · Cited by 340
Self-citationUnsupervised 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
Joint feature synthesis and embedding: Adversarial cross-modal retrieval revisited
Xu, X., Lin, K., Yang, Y., Hanjalic, A., & Shen, H. T. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.3045530 · Cited by 88
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Metadata
- Authors
- Xu, X., Shen, F., Yang, Y., Shen, H. T., & Li, X.
- Venue
- IEEE Transactions on Image Processing
- Published
- 2017-05
- Volume
- 26
- Issue
- 5
- Pages
- 2494-2507
- ISSN
- 1057-7149 (ISSN-L)
- 1941-0042
- OpenAlex
- W2591669147
- Cited by
- 458
- References
- 42
- FWCI
- 22.24
- Type
- journal article
- Language
- en
- Open access
- green
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