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

arXiv: https://arxiv.org/abs/1609.01625 · HTML: https://mlsanity.com/paper/1609.01625/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Sep 2016.

## Built on by (most cited first, 5 of 5)

- [Two new parameters for the ordinal analysis of images](https://mlsanity.com/paper/2212.14643/) (2022)
- [Statistical Properties of the Entropy from Ordinal Patterns](https://mlsanity.com/paper/2209.07650/) (2022)
- [Texture Discrimination via Hilbert Curve Path Based Information Quantifiers](https://mlsanity.com/paper/2409.15327/) (2024)
- [Feature Engineering is Not Dead: Reviving Classical Machine Learning with Entropy, HOG, and LBP Feature Fusion for Image Classification](https://mlsanity.com/paper/2507.13772/) (2025)
- [Interpretable liquid crystal phase classification via two-by-two ordinal patterns](https://mlsanity.com/paper/2603.26723/) (2026)

## Abstract (arXiv)

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.