Abstract
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.
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Acknowledgement
The work was partially supported by the following: National Natural Science Foundation of China under no. 61876155, and 61876154; The Natural Science Foundation of the Jiangsu Higher Education Institutions of China under no. 17KJD520010; Suzhou Science and Technology Program under no. SYG201712, SZS201613; Natural Science Foundation of Jiangsu Province BK20181189 and BK20181190; Key Program Special Fund in XJTLU under no. KSF-A-01, KSF-P-02, KSF-E-26, and KSF-A-10; XJTLU Research Development Fund RDF-16-02-49.
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Hong, J., Huang, K., Liang, HN., Wang, X., Zhang, R. (2020). Fine-Grained Image Classification with Object-Part Model. In: Ren, J., et al. Advances in Brain Inspired Cognitive Systems. BICS 2019. Lecture Notes in Computer Science(), vol 11691. Springer, Cham. https://doi.org/10.1007/978-3-030-39431-8_22
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DOI: https://doi.org/10.1007/978-3-030-39431-8_22
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