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Text-Image Retrieval With Salient Features

Text-Image Retrieval With Salient Features

Xia Feng, Zhiyi Hu, Caihua Liu, W. H. Ip, Huiying Chen
Copyright: © 2021 |Volume: 32 |Issue: 4 |Pages: 13
ISSN: 1063-8016|EISSN: 1533-8010|EISBN13: 9781799859116|DOI: 10.4018/JDM.2021100101
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MLA

Feng, Xia, et al. "Text-Image Retrieval With Salient Features." JDM vol.32, no.4 2021: pp.1-13. http://doi.org/10.4018/JDM.2021100101

APA

Feng, X., Hu, Z., Liu, C., Ip, W. H., & Chen, H. (2021). Text-Image Retrieval With Salient Features. Journal of Database Management (JDM), 32(4), 1-13. http://doi.org/10.4018/JDM.2021100101

Chicago

Feng, Xia, et al. "Text-Image Retrieval With Salient Features," Journal of Database Management (JDM) 32, no.4: 1-13. http://doi.org/10.4018/JDM.2021100101

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Abstract

In recent years, deep learning has achieved remarkable results in the text-image retrieval task. However, only global image features are considered, and the vital local information is ignored. This results in a failure to match the text well. Considering that object-level image features can help the matching between text and image, this article proposes a text-image retrieval method that fuses salient image feature representation. Fusion of salient features at the object level can improve the understanding of image semantics and thus improve the performance of text-image retrieval. The experimental results show that the method proposed in the paper is comparable to the latest methods, and the recall rate of some retrieval results is better than the current work.

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