  This work proposes a novel method based on a pseudo-parabolic diffusion
process to be employed for texture recognition. The proposed operator is
applied over a range of time scales giving rise to a family of images
transformed by nonlinear filters. Therefore each of those images are encoded by
a local descriptor (we use local binary patterns for that purpose) and they are
summarized by a simple histogram, yielding in this way the image feature
vector. The proposed approach is tested on the classification of well
established benchmark texture databases and on a practical task of plant
species recognition. In both cases, it is compared with several
state-of-the-art methodologies employed for texture recognition. Our proposal
outperforms those methods in terms of classification accuracy, confirming its
competitiveness. The good performance can be justified to a large extent by the
ability of the pseudo-parabolic operator to smooth possibly noisy details
inside homogeneous regions of the image at the same time that it preserves
discontinuities that convey critical information for the object description.
Such results also confirm that model-based approaches like the proposed one can
still be competitive with the omnipresent learning-based approaches, especially
when the user does not have access to a powerful computational structure and a
large amount of labeled data for training.
