# A cellular automata approach to local patterns for texture recognition

arXiv: https://arxiv.org/abs/2007.07462 · HTML: https://mlsanity.com/paper/2007.07462/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jul 2020.

## Built on by (most cited first, 3 of 3)

- [Neural Cellular Automata for Lightweight, Robust and Explainable Classification of White Blood Cell Images](https://mlsanity.com/paper/2404.05584/) (2024)
- [Unleashing Foundation Vision Models: Adaptive Transfer for Diverse Data-Limited Scientific Domains](https://mlsanity.com/paper/2512.22664/) (2025)
- [Multi-Texture Synthesis through Signal Responsive Neural Cellular Automata](https://mlsanity.com/paper/2407.05991/) (2024)

## Abstract (arXiv)

Texture recognition is one of the most important tasks in computer vision and, despite the recent success of learning-based approaches, there is still need for model-based solutions. This is especially the case when the amount of data available for training is not sufficiently large, a common situation in several applied areas, or when computational resources are limited. In this context, here we propose a method for texture descriptors that combines the representation power of complex objects by cellular automata with the known effectiveness of local descriptors in texture analysis. The method formulates a new transition function for the automaton inspired on local binary descriptors. It counterbalances the new state of each cell with the previous state, in this way introducing an idea of "controlled deterministic chaos". The descriptors are obtained from the distribution of cell states. The proposed descriptors are applied to the classification of texture images both on benchmark data sets and a real-world problem, i.e., that of identifying plant species based on the texture of their leaf surfaces. Our proposal outperforms other classical and state-of-the-art approaches, especially in the real-world problem, thus revealing its potential to be applied in numerous practical tasks involving texture recognition at some stage.