Hu, Zechao and Bors, Adrian Gheorghe orcid.org/0000-0001-7838-0021 (2022) Dot-Product Based Global and Local Feature Fusion for Image Search. In: IEEE International Conference on Image Processing (ICIP). IEEE , Bordeaux, France , pp. 1911-1915.
Abstract
Content-based image retrieval (CBIR) consists in searching the most similar images to the query content from a given pool of images or database. Existing works' success relies on taking advantage of both local and global feature information leading to better retrieval performance than when using either of these. Lately, CBIR area has been dominated by the two-stage image retrieval framework which utilizes global features to get initial retrieval results, while using local features for re-ranking in a second stage. In this study, instead of utilizing local and global features separately during two stages, we propose to use a dot-product based local and global (DPLG) feature fusion module leading to a comprehensive global feature descriptor. The proposed fusion module is jointly end-to-end trained within the convolution backbone structure. According to the experimental results, the proposed module achieves new state-of-the-art results on some benchmark datasets. Index Terms-Content based image retrieval, Local and global features, Dot-product attention.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 IEEE. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) |
Depositing User: | Pure (York) |
Date Deposited: | 09 Nov 2022 10:30 |
Last Modified: | 26 Mar 2025 00:17 |
Published Version: | https://doi.org/10.1109/ICIP46576.2022.9897661 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/ICIP46576.2022.9897661 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:193133 |
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Description: Dot-Product Based Global and Local Feature Fusion for Image Search