Texture image classification based on a pseudo-parabolic diffusion model

TL;DR

This paper introduces a new texture recognition method using a pseudo-parabolic diffusion model combined with local binary patterns, outperforming existing techniques on benchmark datasets and plant species recognition tasks.

Contribution

It presents a novel pseudo-parabolic diffusion operator for texture analysis, integrating nonlinear filtering with local descriptors for improved classification accuracy.

Findings

Outperforms state-of-the-art texture recognition methods

Effective in both benchmark and practical plant recognition tasks

Preserves important image details while reducing noise

Abstract

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.