Accelerating Image Classification with Graph Convolutional Neural Networks using Voronoi Diagrams - PhD Dissertation Proposal by: Mustafa Mohammadi Gharasuie

Accelerating Image Classification with Graph Convolutional Neural Networks using Voronoi Diagrams

PhD Dissertation Proposal by: Mustafa Mohammadi Gharasuie

Date: Friday, December 12^th, 2025

Time:  1:00 PM

Location: Essex Hall, Room 122

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 perform image classification, leveraging their exceptional capability to model relational data. Unlike 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 propose a faster version of GCN, namely normalized Voronoi Graph Convolutional Network (NVGCN).

Internal Reader: Dr. Alioune Ngom

Internal Reader: Dr. Dan Wu      

External Reader: Dr. Mehdi Sangani Monfared  

Advisor: Dr. Luis Rueda