Graph-Based Image Classification Using Superpixels and Non-Parametric Learning

This paper studies image classification using graph neural networks, where images are represented as graphs obtained from superpixel segmentation. First, images are converted into region-adjacency graphs using the Simple Linear Iterative Clustering (SLIC) superpixel method. Then, graphlevel classification is performed using Graph Attention Networks (GAT) and Graph Isomorphism Networks (GIN). In addition to standard supervised training with a parametric classifier, we evaluate an alternative two-stage approach in which a non-negative kernel classifier is applied on graph embeddings. Experimental results show that the nonparametric classifier consistently outperforms the supervised approach for both architectures, with especially significant improvements observed for GAT.