Fine-grained image classification via metric learning and feature reconstruction networks

The existing fine-grained image classification algorithms focus on mining low-level details to emphasize subtle differences between subcategories. However, by integrating low-level details with high-level semantics, it is easy to introduce some redundant features (such as blurred regions and background noise), which may hinder the network’s understanding of object parts. Therefore, we propose a metric learning and feature reconstruction network to efficiently fuse multi-level features and reduce redundant information. First, the network employs a convolution alignment enhancement module to refine shallow details and uses large kernel convolution to increase the receptive field, capturing a wider range of context information. Second, the network utilizes a metric learning framework to deeply explore inter-layer channel difference information and complementary fusion of these difference features, ensuring the diversity of the fused feature information. Finally, to reduce the interference of redundant information, spatial and channel reconstruction modules are introduced to suppress spatial redundancy and channel redundancy. The proposed method achieves great performance on the three benchmark datasets of CUB-200-2011, FGVC-Aircraft, and Stanford Cars, with accuracy rates of 90.2%, 93.7%, and 95.1%, respectively. The experimental results show that the model is better than the current mainstream fine-grained image classification methods. The classification results are more accurate.