  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.
