Graph Neural Networks for Image Classification : An Empirical Comparison with Convolutional Networks - Rychnovská, Hedvika; Zamazal, Ondřej; Vacura, Miroslav; Vysoká škola ekonomická v Praze. Fakulta informatiky a statistiky (2025)
Image classification is one of the core tasks in computer vision and is commonly addressed using Convolutional Neural Networks (CNNs), which are designed for grid-like data. Graph Neural Networks (GNNs), designed for graph data, offer an alternative approach by representing images as graphs and can be considered as a generalization of CNNs. The goal of this thesis is to review GNNs with a focus on their use in image classification, to select suitable datasets and architectures, and to compare CNNs and GNNs on this task. Images are converted into superpixel graphs using the SLIC algorithm and the Region Adjacency Graph (RAG) method, and the selected models include GCN and GraphSAGE for the GNNs and two custom CNN architectures. The experiments address four aspects: accuracy, trade-off between accuracy and computational cost, the effect of the number of superpixel segments, and per-class classification. The results show that CNNs achieve higher accuracy overall, while GNNs perform comparably only on simpler images but offer advantages in certain efficiency-related cases. The number of superpixel segments used in graph construction was shown to influence their performance. Overall, the thesis provides a theoretical background and an empirical comparison of CNNs and GNNs on image data across several evaluation criteria.
Vedoucí práce: Ondřej Zamazal
Bakalářská práce (Bc.)—Vysoká škola ekonomická v Praze. Fakulta informatiky a statistiky, 2026
Obsahuje bibliografii
Textový (vysokoškolská kvalifikační práce)
Rok obhajoby 2026
Způsob přístupu: Internet
Keywords: data analytics [obor bakal. práce]; bakalářské práce; bachelor's theses