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

arXiv: https://arxiv.org/abs/2102.05001 · HTML: https://mlsanity.com/paper/2102.05001/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 38 references that are in the map.
- First posted on arXiv 9 Feb 2021.

## Builds on (most cited first, 4 of 4)

- [Describing Textures in the Wild](https://mlsanity.com/paper/1311.3618/) (2013)
- [PCANet: A Simple Deep Learning Baseline for Image Classification?](https://mlsanity.com/paper/1404.3606/) (2014)
- [Deep filter banks for texture recognition, description, and segmentation](https://mlsanity.com/paper/1507.02620/) (2015)
- [Texture image classification based on a pseudo-parabolic diffusion model](https://mlsanity.com/paper/2011.07173/) (2020)

## Abstract (arXiv)

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.