TY - GEN
T1 - Fine-grained image classification with object-part model
AU - Hong, Jinlong
AU - Huang, Kaizhu
AU - Liang, Hai Ning
AU - Wang, Xinheng
AU - Zhang, Rui
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2020.
PY - 2020
Y1 - 2020
N2 - Fine-grained image classification is used to identify dozens or hundreds of subcategory images which are classified in a same large category. This task is challenging due to the subtle inter-class visual differences. Most existing methods try to locate discriminative regions or parts of objects to develop an effective classifier. However, there are two main limitations: (1) part annotations or attribute descriptions are usually labor-intensive, and (2) it is less effective to find spatial relationship between the object and its parts. To alleviate these problems, we propose a novel object-part model that relies on an attention mechanism. The main improvements of our method are threefold: (1) an object-part spatial constraint which selects highly representative parts, able to keep parts both discriminative and integrative, (2) a novel heatmap generation method, able to represent comprehensively the discriminative parts by regions, and (3) a speed up of the part selection by filtering image patch candidates using a fine-tuned CNN. With these improvements, the proposed method achieves encouraging results compared to the state-of-the-art methods benchmarking on the Stanford Cars and Oxford-IIIT Pet datasets.
AB - Fine-grained image classification is used to identify dozens or hundreds of subcategory images which are classified in a same large category. This task is challenging due to the subtle inter-class visual differences. Most existing methods try to locate discriminative regions or parts of objects to develop an effective classifier. However, there are two main limitations: (1) part annotations or attribute descriptions are usually labor-intensive, and (2) it is less effective to find spatial relationship between the object and its parts. To alleviate these problems, we propose a novel object-part model that relies on an attention mechanism. The main improvements of our method are threefold: (1) an object-part spatial constraint which selects highly representative parts, able to keep parts both discriminative and integrative, (2) a novel heatmap generation method, able to represent comprehensively the discriminative parts by regions, and (3) a speed up of the part selection by filtering image patch candidates using a fine-tuned CNN. With these improvements, the proposed method achieves encouraging results compared to the state-of-the-art methods benchmarking on the Stanford Cars and Oxford-IIIT Pet datasets.
KW - Fine-grained image classification
KW - Object-part model
KW - Weakly supervised learning
UR - http://www.scopus.com/inward/record.url?scp=85080901586&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-39431-8_22
DO - 10.1007/978-3-030-39431-8_22
M3 - Conference Proceeding
AN - SCOPUS:85080901586
SN - 9783030394301
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 233
EP - 243
BT - Advances in Brain Inspired Cognitive Systems - 10th International Conference, BICS 2019, Proceedings
A2 - Ren, Jinchang
A2 - Hussain, Amir
A2 - Zhao, Huimin
A2 - Cai, Jun
A2 - Chen, Rongjun
A2 - Xiao, Yinyin
A2 - Huang, Kaizhu
A2 - Zheng, Jiangbin
PB - Springer
T2 - 10th International Conference on Brain Inspired Cognitive Systems, BICS 2019
Y2 - 13 July 2019 through 14 July 2019
ER -