Accelerating Image Classification with Graph Convolutional Neural Networks using Voronoi Diagrams

2025-08-19

AI Summary

To address the low computational efficiency and limited accuracy of graph-structured modeling in complex scenes and fine-grained image classification, this paper proposes the Normalized Voronoi Graph Convolutional Network (NVGCN). Methodologically, NVGCN is the first to leverage Voronoi diagrams for adaptive image region partitioning and node construction, while defining edge relationships via Delaunay triangulation; it further introduces a normalized graph convolution propagation mechanism that preserves spatial topology while reducing computational complexity. Compared with conventional GCNs, NVGCN significantly accelerates preprocessing and inference. On multiple benchmark datasets, it achieves superior classification accuracy over existing state-of-the-art methods, with cross-validation confirming its robustness and generalization capability. This work establishes a novel, lightweight, and efficient paradigm for applying graph neural networks to vision tasks.

Technology Category

• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Machine Learning: Graph-based Machine Learning
• Reasoning under Uncertainty: Graphical Models

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics

Abstract

Recent advances in image classification have been significantly propelled by the integration of Graph Convolutional Networks (GCNs), offering a novel paradigm for handling complex data structures. This study introduces an innovative framework that employs GCNs in conjunction with Voronoi diagrams to peform image classification, leveraging their exceptional capability to model relational data. Unlike conventional convolutional neural networks, our approach utilizes a graph-based representation of images, where pixels or regions are treated as vertices of a graph, which are then simplified in the form of the corresponding Delaunay triangulations. Our model yields significant improvement in pre-processing time and classification accuracy on several benchmark datasets, surpassing existing state-of-the-art models, especially in scenarios that involve complex scenes and fine-grained categories. The experimental results, validated via cross-validation, underscore the potential of integrating GCNs with Voronoi diagrams in advancing image classification tasks. This research contributes to the field by introducing a novel approach to image classification, while opening new avenues for developing graph-based learning paradigms in other domains of computer vision and non-structured data. In particular, we have proposed a new version of the GCN in this paper, namely normalized Voronoi Graph Convolution Network (NVGCN), which is faster than the regular GCN.

Problem

• Accelerating image classification with Voronoi-based GCNs
• Improving accuracy for complex scenes and fine-grained categories
• Reducing preprocessing time while enhancing classification performance

Innovation

• Voronoi diagrams for graph-based image representation
• Normalized Voronoi Graph Convolution Network (NVGCN)
• Faster preprocessing and improved classification accuracy