Cross-Modal Hashing Retrieval Based on Density Clustering
Cross-modal hashing retrieval methods have attracted much attention for their effectiveness and efficiency. However, most of the existing hashing methods have the problem of how to precisely learn potential correlations between different modalities from binary codes with minimal loss. In addition, s...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9026921/ |
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| author | Xiaojun Qi Xianhua Zeng Hongmei Tang |
| author_facet | Xiaojun Qi Xianhua Zeng Hongmei Tang |
| author_sort | Xiaojun Qi |
| collection | DOAJ |
| description | Cross-modal hashing retrieval methods have attracted much attention for their effectiveness and efficiency. However, most of the existing hashing methods have the problem of how to precisely learn potential correlations between different modalities from binary codes with minimal loss. In addition, solving binary codes in different modalities is an NP-hard problem. To overcome these challenges, we initially propose a novel adaptive fast cross-modal hashing retrieval method under the inspiration of DBSCAN clustering algorithm, named Cross-modal Hashing Retrieval Based on Density Clustering (DCCH). DCCH utilizes the global density correlation between different modalities to select representative instances to replace the entire data precisely. Furthermore, DCCH excludes the adverse effects of noise points and leverages the discrete optimization process to obtain hash functions. The extensive experiments show that DCCH is superior to other state-of-the-art cross-modal methods on three benchmark bimodal datasets, i.e., Wiki, MIRFlickr and NUS-WIDE. Therefore, the experimental results also prove that our method DCCH is comparatively usable and efficient. |
| format | Article |
| id | doaj-art-d7dabb0787224f09a9c2073e25967d66 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-d7dabb0787224f09a9c2073e25967d662025-08-20T03:40:51ZengIEEEIEEE Access2169-35362025-01-0113445774458910.1109/ACCESS.2020.29788769026921Cross-Modal Hashing Retrieval Based on Density ClusteringXiaojun Qi0https://orcid.org/0000-0002-2577-4649Xianhua Zeng1https://orcid.org/0000-0001-5892-2372Hongmei Tang2https://orcid.org/0000-0002-4198-2595Chongqing Key Laboratory of Image Cognition, College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, ChinaChongqing Key Laboratory of Image Cognition, College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, ChinaChongqing Key Laboratory of Image Cognition, College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, ChinaCross-modal hashing retrieval methods have attracted much attention for their effectiveness and efficiency. However, most of the existing hashing methods have the problem of how to precisely learn potential correlations between different modalities from binary codes with minimal loss. In addition, solving binary codes in different modalities is an NP-hard problem. To overcome these challenges, we initially propose a novel adaptive fast cross-modal hashing retrieval method under the inspiration of DBSCAN clustering algorithm, named Cross-modal Hashing Retrieval Based on Density Clustering (DCCH). DCCH utilizes the global density correlation between different modalities to select representative instances to replace the entire data precisely. Furthermore, DCCH excludes the adverse effects of noise points and leverages the discrete optimization process to obtain hash functions. The extensive experiments show that DCCH is superior to other state-of-the-art cross-modal methods on three benchmark bimodal datasets, i.e., Wiki, MIRFlickr and NUS-WIDE. Therefore, the experimental results also prove that our method DCCH is comparatively usable and efficient.https://ieeexplore.ieee.org/document/9026921/Cross-modal retrievalvariable-length hashing codesdensity clusteringdiscrete optimization |
| spellingShingle | Xiaojun Qi Xianhua Zeng Hongmei Tang Cross-Modal Hashing Retrieval Based on Density Clustering IEEE Access Cross-modal retrieval variable-length hashing codes density clustering discrete optimization |
| title | Cross-Modal Hashing Retrieval Based on Density Clustering |
| title_full | Cross-Modal Hashing Retrieval Based on Density Clustering |
| title_fullStr | Cross-Modal Hashing Retrieval Based on Density Clustering |
| title_full_unstemmed | Cross-Modal Hashing Retrieval Based on Density Clustering |
| title_short | Cross-Modal Hashing Retrieval Based on Density Clustering |
| title_sort | cross modal hashing retrieval based on density clustering |
| topic | Cross-modal retrieval variable-length hashing codes density clustering discrete optimization |
| url | https://ieeexplore.ieee.org/document/9026921/ |
| work_keys_str_mv | AT xiaojunqi crossmodalhashingretrievalbasedondensityclustering AT xianhuazeng crossmodalhashingretrievalbasedondensityclustering AT hongmeitang crossmodalhashingretrievalbasedondensityclustering |