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Texture can be defined as the change of image intensity that forms repetitivepatterns, resulting from physical properties of the object's roughness ordifferences in a reflection on the surface. Considering that texture forms acomplex system of patterns in a non-deterministic way, biodiversity conceptscan help texture characterization in images. This paper proposes a novelapproach capable of quantifying such a complex system of diverse patternsthrough species diversity and richness and taxonomic distinctiveness. Theproposed approach considers each image channel as a species ecosystem andcomputes species diversity and richness measures as well as taxonomic measuresto describe the texture. The proposed approach takes advantage of ecologicalpatterns' invariance characteristics to build a permutation, rotation, andtranslation invariant descriptor. Experimental results on three datasets ofnatural texture images and two datasets of histopathological images have shownthat the proposed texture descriptor has advantages over several texturedescriptors and deep methods.

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