中圖分類號： TP301.6
文獻標識碼： A
DOI：10.16157/j.issn.0258-7998.211980
中文引用格式： 潘新辰，楊小健，秦嶺. 基于雙注意力和多區(qū)域檢測的細粒度圖像分類[J].電子技術(shù)應(yīng)用，2022，48(8)：117-122.
英文引用格式： Pan Xinchen，Yang Xiaojian，Qin Ling. Fine-grained image classification based on dual attentions and multi-region detection[J]. Application of Electronic Technique，2022，48(8)：117-122.

Fine-grained image classification based on dual attentions and multi-region detection

Abstract： Effectively detecting discriminative local areas and more accurately extracting fine-grained features of images will help improve the classification effect of fine-grained images. For this reason, a fine-grained image classification method combining dual attention mechanism and multi-region detection is proposed. Multi-region detection aims to locate discriminative image regions through class label learning, and then extract the features of the discriminative local regions through a feature extraction network and merge them with global features. Similarly, a more precise feature extraction network can extract fine-grained features of an image. Therefore, by combining the dual attention mechanism and multi-region detection, the proposed method respectively achieves 88.3%, 94.5% and 92.3% accuracy on three public fine-grained image datasets, CUB-200-2011, StanfordCars and FGVC Aircraft.

Key words : fine-grained image classification；attention mechanism；regional detection；convolutional neural network；feature extraction；feature group