Semantic feature integration network for fine-grained visual classification

Hui Wang; Yueyang Li; Haichi Luo

doi:10.1117/12.3033185

13 June 2024 Semantic feature integration network for fine-grained visual classification

Hui Wang, Yueyang Li, Haichi Luo

Proceedings Volume 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024); 1318061 (2024) https://doi.org/10.1117/12.3033185
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 2024, Guangzhou, China

Abstract

Fine-Grained Visual Classification (FGVC) is a challenging task due to subtle differences among subordinate categories. Many current FGVC approaches focus on identifying and locating discriminative regions, but neglect the presence of unnecessary features that impair the understanding of object structure. These unnecessary features, including 1) ambiguous parts resulting from the visual similarity in object appearances and 2) noninformative parts (e.g., background noise), can have a significant adverse impact on classification results. To address this limitation, we propose the Semantic Feature Integration network (SFI-Net) to eliminate unnecessary information and reconstructing the semantic relations among discriminative features. Our SFI-Net are carefully designed and can be trained end-to-end in a weakly-supervised way. Extensive experiments on four challenging fine-grained benchmarks demonstrate that our work achieves the state-of-thearts performance. Especially, the classification accuracy of our model on CUB-200-2011 and Stanford Dogs reaches 92.64% and 93.03%, respectively.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Hui Wang, Yueyang Li, and Haichi Luo "Semantic feature integration network for fine-grained visual classification", Proc. SPIE 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 1318061 (13 June 2024); https://doi.org/10.1117/12.3033185

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KEYWORDS

Semantics

Tunable filters

Visualization

Background noise

Classification systems

Feature extraction

Education and training

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