Superpixel Graph

Superpixel graphs represent images as networks of interconnected superpixels (groups of similar pixels), enabling efficient processing of visual data by leveraging both spatial relationships and feature similarities within each superpixel. Current research focuses on developing graph neural network (GNN) architectures to analyze these representations, often incorporating techniques like contrastive learning and structural entropy minimization to improve performance in tasks such as image segmentation, classification, and object detection. This approach offers advantages in computational efficiency and interpretability compared to pixel-wise methods, finding applications in diverse fields including remote sensing, medical image analysis, and urban planning.