Hierarchical Graph Neural Networks with Scale-Aware Readout for Image Classification

This work addresses the importance of incorporating multi-scale information in image representation by proposing a novel approach utilizing hierarchical segmentation and graph neural networks (GNNs). The proposed model, named Hierarchical Image Graph with Scale Importance (HIGSI), leverages hierarchical segmentation to construct graphs that capture relationships between nodes across different scales. This multi-scale representation simultaneously captures intricate details and global context, leading to a richer understanding of image structure than traditional methods. Additionally, a novel Region Graph Readout (RGR) function is introduced to assess the significance of each scale within the graph representation. By combining this multi-scale representation and the RGR function, HIGSI achieves competitive performance on image classification tasks, using smaller graphs or having fewer parameters than existing methods. This work also presents a comparative study with another hierarchical approach and an assessment of HIGSI's components to investigate its decision-making process and its components' contribution to the overall performance.