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Multi-Level Correlation Adversarial Hashing for Cross-Modal Retrieval.
- Source :
- IEEE Transactions on Multimedia; Dec2020, Vol. 22 Issue 12, p3101-3114, 14p
- Publication Year :
- 2020
-
Abstract
- Cross-modal hashing (CMH) has been widely used for similarity search in multimedia retrieval applications, thanks to low storage cost and fast query speed. However, preserving the content similarities in finite-length hash codes between different data modalities is still challenging due to the existing heterogeneity gap. To further address the crucial bottleneck, we propose a Multi-Level Correlation Adversarial Hashing (MLCAH) algorithm to integrate the multi-level correlation information into hash codes. The proposed MLCAH model enjoys several merits. First, to the best of our knowledge, it is the early attempt of leveraging the multi-level correlation information for cross-modal hashing retrieval. Second, we propose global and local semantic alignment mechanisms, which can effectively encode multi-level correlation information, including global information, local information, and label information into hash codes. Third, a label-consistency attention mechanism with adversarial training is designed for exploiting the local cross-modality similarity from multi-modality data. Extensive evaluations on four benchmarks demonstrate that the proposed model brings significant improvements over several state-of-the-art cross-modal hashing methods. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 15209210
- Volume :
- 22
- Issue :
- 12
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Multimedia
- Publication Type :
- Academic Journal
- Accession number :
- 147133054
- Full Text :
- https://doi.org/10.1109/TMM.2020.2969792