Fine-grained image classification with object-part model

Jinlong Hong, Kaizhu Huang*, Hai Ning Liang, Xinheng Wang, Rui Zhang

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review


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.

Original languageEnglish
Title of host publicationAdvances in Brain Inspired Cognitive Systems - 10th International Conference, BICS 2019, Proceedings
EditorsJinchang Ren, Amir Hussain, Huimin Zhao, Jun Cai, Rongjun Chen, Yinyin Xiao, Kaizhu Huang, Jiangbin Zheng
Number of pages11
ISBN (Print)9783030394301
Publication statusPublished - 2020
Event10th International Conference on Brain Inspired Cognitive Systems, BICS 2019 - Guangzhou, China
Duration: 13 Jul 201914 Jul 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11691 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference10th International Conference on Brain Inspired Cognitive Systems, BICS 2019


  • Fine-grained image classification
  • Object-part model
  • Weakly supervised learning


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