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Deep Multi-Semantic Fusion-Based Cross-Modal Hashing
| Content Provider | MDPI |
|---|---|
| Author | Zhu, Xinghui Cai, Liewu Zou, Zhuoyang Zhu, Lei |
| Copyright Year | 2022 |
| Description | Due to the low costs of its storage and search, the cross-modal retrieval hashing method has received much research interest in the big data era. Due to the application of deep learning, the cross-modal representation capabilities have risen markedly. However, the existing deep hashing methods cannot consider multi-label semantic learning and cross-modal similarity learning simultaneously. That means potential semantic correlations among multimedia data are not fully excavated from multi-category labels, which also affects the original similarity preserving of cross-modal hash codes. To this end, this paper proposes deep multi-semantic fusion-based cross-modal hashing (DMSFH), which uses two deep neural networks to extract cross-modal features, and uses a multi-label semantic fusion method to improve cross-modal consistent semantic discrimination learning. Moreover, a graph regularization method is combined with inter-modal and intra-modal pairwise loss to preserve the nearest neighbor relationship between data in Hamming subspace. Thus, DMSFH not only retains semantic similarity between multi-modal data, but integrates multi-label information into modal learning as well. Extensive experimental results on two commonly used benchmark datasets show that our DMSFH is competitive with the state-of-the-art methods. |
| Starting Page | 430 |
| e-ISSN | 22277390 |
| DOI | 10.3390/math10030430 |
| Journal | Mathematics |
| Issue Number | 3 |
| Volume Number | 10 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-01-29 |
| Access Restriction | Open |
| Subject Keyword | Mathematics Industrial Engineering Cross-modal Hashing Semantic Label Information Multi-label Semantic Fusion Graph Regularization Deep Neural Network |
| Content Type | Text |
| Resource Type | Article |
