An application of a pseudo-parabolic modeling to texture image recognition

TL;DR

This paper introduces a novel pseudo-parabolic PDE-based method for texture image recognition, leveraging local descriptors and demonstrating competitive accuracy against deep learning on benchmark datasets.

Contribution

It presents a new PDE modeling approach for texture recognition that is effective without large training data, offering an alternative to deep learning methods.

Findings

Achieved promising classification accuracy on benchmark datasets.

Demonstrated competitiveness with modern deep learning approaches.

Opened avenues for PDE-based image analysis in data-scarce scenarios.

Abstract

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.