Persistent Homology and Channel Attention Based Feature Extraction for Image Classification

Transformers and their variants have demonstrated remarkable performance in image classification tasks. MobileViT, as a lightweight architecture, has gained significant attention for real-time image processing due to its computational efficiency and adaptability to resource-constrained environments. It exhibits strong robustness to variations in shape, scale, and viewpoint. However, it still struggle to capture dimensional features and internal relationships within objects. In this paper, we propose a novel feature extraction module called persistent homology with channel attention (PHCA) for image classification. Persistent homology is employed to extract topological features and capture complex spatial relationships across multiple image scales. A channel attention (CA) mechanism is integrated with PH to direct the model's focus toward informative channels, thereby enhancing relevant features while suppressing noise. Furthermore, experiments on fusing PH-based and original features at different network layers indicate that early fusion yields the best performance. To evaluate the effectiveness of our feature extraction method, we conducted experiments on diverse datasets. The results demonstrate that our approach outperforms baseline models in classification accuracy, highlighting the potential of integrating topological features with deep learning for advanced image classification tasks.