  This work proposes the combination of multiscale transform with fractal
descriptors employed in the classification of gray-level texture images. We
apply the space-scale transform (derivative + Gaussian filter) over the
Bouligand-Minkowski fractal descriptors, followed by a threshold over the
filter response, aiming at attenuating noise effects caused by the final part
of this response. The method is tested in the classification of a well-known
data set (Brodatz) and compared with other classical texture descriptor
techniques. The results demonstrate the advantage of the proposed approach,
achieving a higher success rate with a reduced amount of descriptors.
