  In this work, we present a novel methodology for texture image recognition
using a partial differential equation modeling. More specifically, we employ
the pseudo-parabolic Buckley-Leverett equation to provide a dynamics to the
digital image representation and collect local descriptors from those images
evolving in time. For the local descriptors we employ the magnitude and signal
binary patterns and a simple histogram of these features was capable of
achieving promising results in a classification task. We compare the accuracy
over well established benchmark texture databases and the results demonstrate
competitiveness, even with the most modern deep learning approaches. The
achieved results open space for future investigation on this type of modeling
for image analysis, especially when there is no large amount of data for
training deep learning models and therefore model-based approaches arise as
suitable alternatives.
