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However, existing supervised methods often overlook noisy labels and multiscale features in different modal datasets, leading to higher information entropy in the generated hash codes and features, which reduces retrieval performance. The variation in text annotation information across datasets further increases the information entropy during text feature extraction, resulting in suboptimal outcomes. Consequently, reducing the information entropy in text feature extraction, supplementing text feature information, and enhancing the retrieval efficiency of large-scale media data are critical challenges in cross-modal retrieval research. To tackle these, this paper introduces the Text-Enhanced Graph Attention Hashing for Cross-Modal Retrieval (TEGAH) framework. TEGAH incorporates a deep text feature extraction network and a multiscale label region fusion network to minimize information entropy and optimize feature extraction. Additionally, a Graph-Attention-based modal feature fusion network is designed to efficiently integrate multimodal information, enhance the affinity of the network for different modes, and retain more semantic information. Extensive experiments on three multilabel datasets demonstrate that the TEGAH framework significantly outperforms state-of-the-art cross-modal hashing methods.<\/jats:p>","DOI":"10.3390\/e26110911","type":"journal-article","created":{"date-parts":[[2024,10,28]],"date-time":"2024-10-28T08:39:07Z","timestamp":1730104747000},"page":"911","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Text-Enhanced Graph Attention Hashing for Cross-Modal Retrieval"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-1321-0391","authenticated-orcid":false,"given":"Qiang","family":"Zou","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, Xinjiang University, Urumqi 830046, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8363-8832","authenticated-orcid":false,"given":"Shuli","family":"Cheng","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Xinjiang University, Urumqi 830046, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9744-9589","authenticated-orcid":false,"given":"Anyu","family":"Du","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Xinjiang University, Urumqi 830046, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiayi","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Xinjiang University, Urumqi 830046, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"566","DOI":"10.1016\/j.inffus.2022.11.017","article-title":"A survey on cross-media search based on user intention understanding in social networks","volume":"91","author":"Shi","year":"2023","journal-title":"Inf. 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