Fast Graph Neural Network for Image Classification

Due to their inherent ability to process data in a graph-based format, GCNs are especially well-suited for applications where relational context and structural information play a crucial role. This introduction explores recent advancements in image classification using GCNs, with a particular focus on the integration of superpixels and wavelet techniques. Graph Convolutional Networks (GCNs) have become a powerful framework for image classification, largely due to their ability to capture and process the non-Euclidean structure of data. Zhou et al. provided a comprehensive review of GCN applications across various domains, highlighting their effectiveness in image classification tasks [1]. Their study emphasized that, unlike traditional Convolutional Neural Networks (CNNs), GCNs excel at capturing long-range dependencies and complex relational patterns within images. Meanwhile, wavelet techniques [2] have transformed image processing by introducing a multi-resolution analysis framework that is essential for applications such as image compression, feature extraction, and noise reduction. In this context, Wavelet transforms have been utilized in conjunction with GCNs for feature extraction in image classification, denoising, compression and other tasks. Wavelets provide a multi-resolution image analysis, capturing spatial and frequency domain information [3]. This paper presents an innovative framework that integrates Graph Convolutional Networks (GCNs) with Voronoi diagrams for image classification, leveraging their exceptional ability to model relational data.