Discriminating image textures with the multiscale two-dimensional complexity-entropy causality plane

TL;DR

This paper introduces a multiscale complexity-entropy causality plane to effectively distinguish and characterize image textures across different spatial scales, enhancing texture analysis capabilities.

Contribution

It presents a novel multiscale extension of the complexity-entropy causality plane for improved texture discrimination in images.

Findings

Successfully distinguishes textures at multiple scales

Unveils intrinsic spatial correlations in images

Proves practical for texture characterization

Abstract

The aim of this paper is to further explore the usefulness of the two-dimensional complexity-entropy causality plane as a texture image descriptor. A multiscale generalization is introduced in order to distinguish between different roughness features of images at small and large spatial scales. Numerically generated two-dimensional structures are initially considered for illustrating basic concepts in a controlled framework. Then, more realistic situations are studied. Obtained results allow us to confirm that intrinsic spatial correlations of images are successfully unveiled by implementing this multiscale symbolic information-theory approach. Consequently, we conclude that the proposed representation space is a versatile and practical tool for identifying, characterizing and discriminating image textures.