Aggregating Object Features Based on Attention Weights for Fine-Grained Image Retrieval

Object localization and local feature representation are key issues in fine-grained image retrieval. However, the existing unsupervised methods still need to be improved in these two aspects. For conquering these issues in a unified framework, a novel unsupervised scheme, named DSAW for short, is pr...

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Bibliographic Details
Published in2020 25th International Conference on Pattern Recognition (ICPR) pp. 2838 - 2844
Main Authors Lin, HongLi, Song, Yongqi, Zeng, Zixuan, Wang, Weisheng, Wang, Jiayi
Format Conference Proceeding
LanguageEnglish
Published IEEE 10.01.2021
Subjects
Feature extraction
Image retrieval
Location awareness
Pattern recognition
Weight measurement
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Summary:Object localization and local feature representation are key issues in fine-grained image retrieval. However, the existing unsupervised methods still need to be improved in these two aspects. For conquering these issues in a unified framework, a novel unsupervised scheme, named DSAW for short, is presented in this paper. Firstly, we proposed a dual-selection (DS) method, which achieves more accurate object localization by using an adaptive threshold method to perform feature selection on local and global activation map. Secondly, a novel and faster self-attention weights (AW) method is developed to weight local features by measuring their importance in the global context. Finally, we also evaluated the performance of the proposed method on five fine-grained image datasets and the results showed that our DSAW outperformed the existing best method.
DOI:10.1109/ICPR48806.2021.9412435