Fast Graph Neural Network for Image Classification

2025-08-20

AI Summary

To address insufficient relational modeling in complex and fine-grained image classification, this paper proposes a novel method integrating Voronoi diagrams with Graph Convolutional Networks (GCNs). First, image pixels or regions are mapped to graph nodes; then, geometrically aware adjacency structures are constructed via Voronoi tessellation and Delaunay triangulation—replacing conventional handcrafted or distance-threshold-based graph topologies. Subsequently, a GCN module is designed to learn structured feature representations. This work pioneers the incorporation of Voronoi diagrams into GCN-based image classification, leveraging geometric priors to enhance graph structural expressiveness. Extensive experiments on multiple benchmark datasets demonstrate substantial improvements: an average accuracy gain of +2.3%, 1.8× acceleration in graph construction time, and superior generalization over state-of-the-art graph-based and CNN-based methods. These results validate the efficacy of geometry-guided graph learning for visual recognition tasks.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Computer Vision: Representation Learning for Vision
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Content-based information diffusion

Abstract

The rapid progress in image classification has been largely driven by the adoption of Graph Convolutional Networks (GCNs), which offer a robust framework for handling complex data structures. This study introduces a novel approach that integrates GCNs with Voronoi diagrams to enhance image classification by leveraging their ability to effectively model relational data. Unlike conventional convolutional neural networks (CNNs), our method represents images as graphs, where pixels or regions function as vertices. These graphs are then refined using corresponding Delaunay triangulations, optimizing their representation. The proposed model achieves significant improvements in both preprocessing efficiency and classification accuracy across various benchmark datasets, surpassing state-of-the-art approaches, particularly in challenging scenarios involving intricate scenes and fine-grained categories. Experimental results, validated through cross-validation, underscore the effectiveness of combining GCNs with Voronoi diagrams for advancing image classification. This research not only presents a novel perspective on image classification but also expands the potential applications of graph-based learning paradigms in computer vision and unstructured data analysis.

Problem

• Enhancing image classification using GCNs and Voronoi diagrams
• Representing images as graphs with optimized Delaunay triangulation structures
• Improving preprocessing efficiency and accuracy for complex scene classification

Innovation

• Integrates GCNs with Voronoi diagrams
• Represents images as graphs using vertices
• Refines graphs via Delaunay triangulations optimization