Searching and Learning Discriminative Regions for Fine-Grained Image Retrieval and Classification

Kangbo SUN

IEICE TRANSACTIONS on Information and Systems   Vol.E105-D    No.1    pp.141-149
Publication Date: 2022/01/01
Publicized: 2021/10/18
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2021EDP7094
Type of Manuscript: PAPER
Category: Image Recognition, Computer Vision
fine-grained,  image retrieval,  image classification,  attention,  metric learning,  

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Local discriminative regions play important roles in fine-grained image analysis tasks. How to locate local discriminative regions with only category label and learn discriminative representation from these regions have been hot spots. In our work, we propose Searching Discriminative Regions (SDR) and Learning Discriminative Regions (LDR) method to search and learn local discriminative regions in images. The SDR method adopts attention mechanism to iteratively search for high-response regions in images, and uses this as a clue to locate local discriminative regions. Moreover, the LDR method is proposed to learn compact within category and sparse between categories representation from the raw image and local images. Experimental results show that our proposed approach achieves excellent performance in both fine-grained image retrieval and classification tasks, which demonstrates its effectiveness.

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