Topological data analysis and image visibility graph for texture classification

Abstract

The texture of an image is an important component that plays a significant role in its recognition. The field of computer vision encounters various problems, including tasks such as image segmentation and classification, which heavily rely on the fundamental principles of texture analysis. Textures have facilitated the identification of diverse images, such as those related to satellites, forestry, and medicine. The objective of this study is to provide a method for classifying textures using picture visibility graphs and topological data analysis. This study presents an innovative strategy that integrates topological data analysis with image visibility graphs. The current investigation entails analysing the degree distribution derived from the visibility graph as well as extracting seven distinct topological features. These features are then employed for classification purposes. The suggested methodology has been assessed using two well-known established image texture datasets: the Brodatz texture image dataset and the KTH-TIPS dataset. The findings indicate the potential for incorporating graph-based methodologies and topological attributes in the context of texture classification.