# Two new parameters for the ordinal analysis of images

arXiv: https://arxiv.org/abs/2212.14643 · HTML: https://mlsanity.com/paper/2212.14643/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 25 references that are in the map.
- First posted on arXiv 30 Dec 2022.

## Builds on (most cited first, 2 of 2)

- [Discriminating image textures with the multiscale two-dimensional complexity-entropy causality plane](https://mlsanity.com/paper/1609.01625/) (2016)
- [Estimating physical properties from liquid crystal textures via machine learning and complexity-entropy methods](https://mlsanity.com/paper/1901.01754/) (2019)

## Built on by (most cited first, 1 of 1)

- [Interpretable liquid crystal phase classification via two-by-two ordinal patterns](https://mlsanity.com/paper/2603.26723/) (2026)

## Abstract (arXiv)

Local patterns play an important role in statistical physics as well as in image processing. Two-dimensional ordinal patterns were studied by Ribeiro et al. who determined permutation entropy and complexity in order to classify paintings and images of liquid crystals. Here we find that the 2 by 2 patterns of neighboring pixels come in three types. The statistics of these types, expressed by two parameters, contains the relevant information to describe and distinguish textures. The parameters are most stable and informative for isotropic structures.