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

TL;DR

This paper introduces a new texture descriptor inspired by biodiversity and taxonomy concepts, effectively capturing complex, non-deterministic patterns in images with invariance to common transformations.

Contribution

It presents a novel bio-inspired texture descriptor that models image channels as ecosystems, using ecological measures for robust texture characterization.

Findings

Outperforms several existing texture descriptors

Effective on natural and histopathological textures

Invariant to permutation, rotation, and translation

Abstract

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.