Image classification method based on superpixels and Graph Neural Network

School of mathematics, Harbin Institute of Technology, 91944, Harbin, Heilongjiang, China

^* Corresponding author: 2021112754@stu.hit.edu.cn

Abstract

In image classification tasks, commonly used deep learning network architectures include Convolutional Neural Network (CNN) and Vision Transformer (ViT). Still, both are relatively mature, while Graph Neural Network (GNN) network architecture has been attempted less in image classification. In addition, the architecture and design of GNN are still rapidly developing and have enormous potential. This study aims to create an image classification method based on superpixels and graph neural networks. This project attempted three different approaches to preserve superpixel node features with varying numbers of features, designed two training network architectures with different complexities and conducted six sets of tests on image classification tasks on the fashion-mnist dataset. As a result, the use of superpixel and GNN methods achieved good accuracy in image classification tasks, demonstrating the potential of this method in image recognition tasks. It was also found that adding the boundary coordinates of the region to the training in the selection of superpixel node features can improve the accuracy of the final image classification task.