Multi-Scale Image Graph Representation: A Novel GNN Approach for Image Classification through Scale Importance Estimation

Image representation as graphs can enhance the understanding of image semantics and facilitate multi-scale image representation. However, existing methods often overlook the significance of the relationship between elements at each scale or fail to encode the hierarchical relationship between graph elements. To cope with that, we introduce a novel approach for graph construction from images. This approach utilizes a hierarchical image segmentation technique to generate segmentation at multiple scales and incorporates edges to encode the relationships at each scale. We also propose a new readout function that weighs the importance of each scale when deriving a fixed-size graph representation. Furthermore, we present a new model incorporating those ideas – called Hierarchical Image Graph with Scale Importance (HIGSI). Experimental results on the CIFAR-10 database indicate that our proposed model outperforms (or closely matches) state-of-the-art and baseline models while utilizing smaller graphs.