Graph entropies in texture segmentation of images

TL;DR

This paper explores the use of entropy-based graph descriptors derived from morphological amoebas for texture segmentation in images, demonstrating their effectiveness and analyzing the information they encode about textures.

Contribution

The study introduces entropy-based graph texture descriptors into a geodesic active contour framework for image segmentation, linking them to fractal dimension measures.

Findings

Effective texture segmentation demonstrated on synthetic and real images.

Entropy-based descriptors relate to fractal dimensions in texture analysis.

Provides insights into the information encoded by graph-based texture descriptors.

Abstract

We study the applicability of a set of texture descriptors introduced in recent work by the author to texture-based segmentation of images. The texture descriptors under investigation result from applying graph indices from quantitative graph theory to graphs encoding the local structure of images. The underlying graphs arise from the computation of morphological amoebas as structuring elements for adaptive morphology, either as weighted or unweighted Dijkstra search trees or as edge-weighted pixel graphs within structuring elements. In the present paper we focus on texture descriptors in which the graph indices are entropy-based, and use them in a geodesic active contour framework for image segmentation. Experiments on several synthetic and one real-world image are shown to demonstrate texture segmentation by this approach. Forthermore, we undertake an attempt to analyse selected entropy-based texture descriptors with regard to what information about texture they actually encode. Whereas this analysis uses some heuristic assumptions, it indicates that the graph-based texture descriptors are related to fractal dimension measures that have been proven useful in texture analysis.