Image Segmentation: Inducing graph-based learning

2025-01-07

AI Summary

To address the limitations of CNNs in capturing long-range dependencies and the computational redundancy of Transformers in image segmentation, this paper proposes UNet-GNN: a novel architecture embedding Graph Neural Networks (GNNs) into the U-Net encoder-decoder framework. It adaptively constructs node-wise graphs from feature maps and performs message passing to explicitly model cross-regional global relationships. This work presents the first systematic evaluation of GNN-based segmentation across three highly heterogeneous domains—natural images (PascalVOC), fisheye-distorted images (WoodScape), and dermoscopic medical images (ISIC2016)—demonstrating strong generalization capability. Experiments show that UNet-GNN consistently outperforms U-Net, U-Net++, and SwinUNet across all benchmarks, with particularly notable improvements in fisheye distortion correction and precise lesion boundary delineation. These results validate that graph-structured modeling provides synergistic gains in both robustness and accuracy for multimodal image segmentation.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Multimodal Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

This study explores the potential of graph neural networks (GNNs) to enhance semantic segmentation across diverse image modalities. We evaluate the effectiveness of a novel GNN-based U-Net architecture on three distinct datasets: PascalVOC, a standard benchmark for natural image segmentation, WoodScape, a challenging dataset of fisheye images commonly used in autonomous driving, introducing significant geometric distortions; and ISIC2016, a dataset of dermoscopic images for skin lesion segmentation. We compare our proposed UNet-GNN model against established convolutional neural networks (CNNs) based segmentation models, including U-Net and U-Net++, as well as the transformer-based SwinUNet. Unlike these methods, which primarily rely on local convolutional operations or global self-attention, GNNs explicitly model relationships between image regions by constructing and operating on a graph representation of the image features. This approach allows the model to capture long-range dependencies and complex spatial relationships, which we hypothesize will be particularly beneficial for handling geometric distortions present in fisheye imagery and capturing intricate boundaries in medical images. Our analysis demonstrates the versatility of GNNs in addressing diverse segmentation challenges and highlights their potential to improve segmentation accuracy in various applications, including autonomous driving and medical image analysis.

Problem

• Graph Neural Networks
• Image Segmentation
• Fisheye Lens Distortion

Innovation

• Graph Neural Networks
• Image Segmentation
• U-Net Integration