Fine-Grained Image Classification Algorithm Using Multi-Scale Feature Fusion and Re-Attention Mechanism

Fine-grained image classification aims to precisely classify an image subclass under a certain category. Hence, it has become a commonand difficult point in the field of computer vision and pattern recognition and has important research value due to its similar features, different gestures, and background interference. The key issue in fine-grained image classification is how to extract precise features from the discriminative region of an image. Existing algorithms based on neural networks are still insufficient in fine feature extraction. Accordingly, a fine-grained image classification algorithm using multi-scale re-attention mechanism is proposed in this study. Considering that high- and low-level features have rich semantic and texture information, respectively, attention mechanism is embedded in different scales to obtain rich feature information. In addition, an input feature map is processed with both channel and spatial attention, which can be regarded as the re-attention of a feature matrix. Finally, using the residual form to combine the attention results and original input feature maps, the attention results on the feature maps of different scales are concatenated and fed into the full connection layer. Thus, accurately extracting salient features is helpful. Accuracy rates of 86.16%, 92.26%, and 93.40% are obtained on the international public fine-grained datasets(CUB-200-2011, FGVC Aircraft, and Stanford Cars). Compared with ResNet50, the accuracy rate is increased by 1.66%, 1.46%, and 1.10%, respectively. It is obviously higher than that of existing classical algorithms and human performance, which demonstrate the effectiveness of the proposed algorithm. © 2020, Editorial Board of Journal of Tianjin University(Science and Technology). All right reserved.