An application of a pseudo-parabolic modeling to texture image recognition
This work offers a model-based alternative for texture image recognition, particularly useful for scenarios with limited training data where deep learning models may struggle.
This paper proposes a texture image recognition method using the pseudo-parabolic Buckley-Leverett equation to model image dynamics and extract local descriptors. The method achieved competitive accuracy on benchmark texture databases, comparable to modern deep learning approaches.
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.