Topological data analysis and image visibility graph for texture classification

Texture, a crucial element in image recognition, presents challenges in computer vision tasks like segmentation and classification. This study aims to introduce a novel approach to texture classification by leveraging picture visibility graphs and topological data analysis. Our method integrates topological data analysis with image visibility graphs, offering an innovative strategy. This study focus on analyzing the degree distribution from the visibility graph and extracting seven distinct topological features. These features serve as the foundation for classification purposes. To validate our methodology, we conducted experiments using two renowned image texture datasets: the Brodatz texture image dataset and the KTH-TIPS dataset. The results underscore the potential of integrating graph-based methodologies and topological attributes in the domain of texture classification. This study signifies a significant advancement in texture analysis, demonstrating the efficacy of combining graph-based representations and topological features for accurate and robust classification.