  Despite the recent success of convolutional neural networks in texture
recognition, model-based descriptors are still competitive, especially when we
do not have access to large amounts of annotated data for training and the
interpretation of the model is an important issue. Among the model-based
approaches, fractal geometry has been one of the most popular, especially in
biological applications. Nevertheless, fractals are part of a much broader
family of models, which are the non-linear operators, studied in chaos theory.
In this context, we propose here a chaos-based local descriptor for texture
recognition. More specifically, we map the image into the three-dimensional
Euclidean space, iterate a chaotic map over this three-dimensional structure
and convert it back to the original image. From such chaos-transformed image at
each iteration we collect local descriptors (here we use local binary patters)
and those descriptors compose the feature representation of the texture. The
performance of our method was verified on the classification of benchmark
databases and in the identification of Brazilian plant species based on the
texture of the leaf surface. The achieved results confirmed our expectation of
a competitive performance, even when compared with some learning-based modern
approaches in the literature.
