  To recognize textures many methods have been developed along the years.
However, texture datasets may be hard to be classified due to artefacts such as
a variety of scale, illumination and noise. This paper proposes the application
of binary distance transform on the original dataset to add information to
texture representation and consequently improve recognition. Texture images,
usually in grayscale, suffers a binarization prior to distance transform and
one of the resulted images are combined with original texture to improve the
amount of information. Four datasets are used to evaluate our approach. For
Outex dataset, for instance, the proposal outperforms all rates, improvements
of an up to 10\%, compared to traditional approach where descriptors are
applied on the original dataset, showing the importance of this approach.
