The School of Computer Science at the University of Windsor is pleased to present …
Fast Graph Neural Network for Image Classification
PhD. Seminar by: Mustafa Mohammadi Gharasuie
Date: Thursday, July 3rd, 2025
Time: 3:00 PM
Location: Essex Hall, Room 122
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 significantly improves 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 presents a novel perspective on image classification and expands the potential applications of graph-based learning paradigms in computer vision and unstructured data analysis.
Internal Reader: Dr. Alioune Ngom
Internal Reader: Dr. Hossein Fani
External Reader: Dr. Mohammad Hassanzadeh
Advisor (s): Dr. Luis Rueda
