Combining Multi-Feature Regions for Fine-Grained Image Recognition
Автор: Sun Fayou, Hea Choon Ngo, Yong Wee Sek
Журнал: International Journal of Image, Graphics and Signal Processing @ijigsp
Статья в выпуске: 1 vol.14, 2022 года.
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Fine-grained visual classification(FGVC) is challenging task duo to the subtle discriminative features.Recently, RA-CNN selects a single feature region of the image, and recursively learns the discriminative features. However, RA-CNN abandons most of feature regions, which is not only the inefficient but aslo ineffective.To address above issues,we design a noval fine-grained visual recognition model MRA-CNN,which associates multi-feature regions.To improve the feature representation,attention blocks are integrated into the backbone to reinforce significant features;To improve the classification accuracy, we design the feature scale dependent(FSD) algorithm to select the optimal outputs as the classifier inputs;To avoid missing features, we adopt the k-means algorithm to select multiple feature regions.We demonstrate the value of MRA-CNN by expensive experiments on three popular fine-grained benchmarks:CUB-200-2011,Cars196 and Aircrafts100 where we achieve state-of-the-art performance.Our codes can be found at https://github.com/dlearing/MRA-CNN.git.
MRA-CNN, reinforce significant features, feature scale dependent, multi-feature regions
Короткий адрес: https://sciup.org/15018302
IDR: 15018302 | DOI: 10.5815/ijigsp.2022.01.02
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