Few-shot image classification algorithm based on deep learning and feature fusion
Few-shot image classification remains difficult because a model must identify novel classes from only one or a few labeled examples while preserving discriminative local information. Metric-learning methods based on Earth Mover’s Distance (EMD) improve local correspondence by representing an image as a set of regional embeddings, but standard backbones treat feature channels uniformly and may retain irrelevant responses. This study proposes ECA-DeepEMD, a channel-attentive metric-learning framework that inserts efficient channel attention (ECA) into a ResNet-12 feature extractor and then computes structural similarity through differentiable EMD. The ECA module captures local cross-channel dependencies without dimensionality reduction, allowing the embedding network to emphasize semantically informative channels with limited computational overhead. The model was evaluated under 5-way 1-shot and 5-way 5-shot protocols on Mini-ImageNet, Tiered-ImageNet, and FC100. It achieved accuracies of 67.14% and 84.90% on Mini-ImageNet, 73.51% and 88.54% on Tiered-ImageNet, and 48.20% and 65.58% on FC100, respectively. Against the reported DeepEMD baseline, the gains on Mini-ImageNet were 1.23 and 2.49 percentage points. Grad-CAM visualizations further indicate that channel attention concentrates responses on category-relevant regions. These findings show that lightweight channel recalibration complements optimal-transport-based local matching and provides a practical approach to data-constrained visual recognition.