IEICE TRANSACTIONS on Information
Searching and Learning Discriminative Regions for Fine-Grained Image Retrieval and Classification
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Information 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
Authors
Kangbo SUN
Shanghai Jiao Tong University,Shanghai Frontier Science Research Center for Gravitational Wave Detection
Jie ZHU
Shanghai Jiao Tong University,Shanghai Frontier Science Research Center for Gravitational Wave Detection
Keyword
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Kangbo SUN, Jie ZHU, "Searching and Learning Discriminative Regions for Fine-Grained Image Retrieval and Classification" in IEICE TRANSACTIONS on Information,
vol. E105-D, no. 1, pp. 141-149, January 2022, doi: 10.1587/transinf.2021EDP7094.
Abstract: 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.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2021EDP7094/_p
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@ARTICLE{e105-d_1_141,
author={Kangbo SUN, Jie ZHU, },
journal={IEICE TRANSACTIONS on Information},
title={Searching and Learning Discriminative Regions for Fine-Grained Image Retrieval and Classification},
year={2022},
volume={E105-D},
number={1},
pages={141-149},
abstract={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.},
keywords={},
doi={10.1587/transinf.2021EDP7094},
ISSN={1745-1361},
month={January},}
Copy
TY - JOUR
TI - Searching and Learning Discriminative Regions for Fine-Grained Image Retrieval and Classification
T2 - IEICE TRANSACTIONS on Information
SP - 141
EP - 149
AU - Kangbo SUN
AU - Jie ZHU
PY - 2022
DO - 10.1587/transinf.2021EDP7094
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E105-D
IS - 1
JA - IEICE TRANSACTIONS on Information
Y1 - January 2022
AB - 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.
ER -
English
