# A Novel Bio-Inspired Texture Descriptor based on Biodiversity and Taxonomic Measures

arXiv: https://arxiv.org/abs/2102.06997 · HTML: https://mlsanity.com/paper/2102.06997/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 75 references that are in the map.
- First posted on arXiv 13 Feb 2021.

## Builds on (most cited first, 8 of 8)

- [Explaining Explanations: An Overview of Interpretability of Machine Learning](https://mlsanity.com/paper/1806.00069/) (2018)
- [From BoW to CNN: Two Decades of Texture Representation for Texture Classification](https://mlsanity.com/paper/1801.10324/) (2018)
- [Wavelet Convolutional Neural Networks for Texture Classification](https://mlsanity.com/paper/1707.07394/) (2017)
- [Using Filter Banks in Convolutional Neural Networks for Texture Classification](https://mlsanity.com/paper/1601.02919/) (2016)
- [Breast Cancer Classification from Histopathological Images with Inception Recurrent Residual Convolutional Neural Network](https://mlsanity.com/paper/1811.04241/) (2018)
- [Data Augmentation for Histopathological Images Based on Gaussian-Laplacian Pyramid Blending](https://mlsanity.com/paper/2002.00072/) (2020)
- [Texture CNN for Histopathological Image Classification](https://mlsanity.com/paper/1905.12005/) (2019)
- [Texture CNN for Thermoelectric Metal Pipe Image Classification](https://mlsanity.com/paper/1905.12003/) (2019)

## Built on by (most cited first, 2 of 2)

- [Texture Characterization of Histopathologic Images Using Ecological Diversity Measures and Discrete Wavelet Transform](https://mlsanity.com/paper/2202.13270/) (2022)
- [Multiscale Analysis for Improving Texture Classification](https://mlsanity.com/paper/2204.09841/) (2022)

## Abstract (arXiv)

Texture can be defined as the change of image intensity that forms repetitive patterns, resulting from physical properties of the object's roughness or differences in a reflection on the surface. Considering that texture forms a complex system of patterns in a non-deterministic way, biodiversity concepts can help texture characterization in images. This paper proposes a novel approach capable of quantifying such a complex system of diverse patterns through species diversity and richness and taxonomic distinctiveness. The proposed approach considers each image channel as a species ecosystem and computes species diversity and richness measures as well as taxonomic measures to describe the texture. The proposed approach takes advantage of ecological patterns' invariance characteristics to build a permutation, rotation, and translation invariant descriptor. Experimental results on three datasets of natural texture images and two datasets of histopathological images have shown that the proposed texture descriptor has advantages over several texture descriptors and deep methods.