Few-shot Fine-grained Image Classification Based on Neighborhood Fusion and Feature Enhancement

Currently fine-grained image classification faces challenges such as labeling difficulties, scarce sample numbers, and subtle category differences. To address these issues, a few-shot fine-grained image classification method based on neighborhood fusion and feature enhancement is proposed. First, the Discrete Cosine Transform (DCT) and channel attention mechanisms are used to capture global and local information from images, respectively. These features are then concatenated along the channel dimensions. This method of combining spatial- and frequency-domain feature extraction enhances the diversity of sample features and improves model generalization. Second, a feature enhancement module is introduced to compute the correlation between query samples and support class prototypes, generating adaptive weights to guide query information to complement the detailed learning of support sample images. This process effectively captures the differences between images of the same class and suppresses local similarities between different classes. Finally, a dual-similarity measurement module assesses the correlation scores between the support class prototypes and the images to be classified, improving the accuracy of classification performance. The experimental results show that this method achieves accuracies of 79.22%, 87.47%, 79.23%, and 83.71% on the 5-shot tasks in the Mini-ImageNet, CUB-200-2011, Stanford Dogs, and Stanford Cars datasets, respectively, outperforming comparative methods.