A Novel Bio-Inspired Texture Descriptor based on Biodiversity and Taxonomic Measures
This work addresses texture analysis for computer vision applications, particularly in medical imaging, but appears incremental as it builds on ecological concepts for texture description.
The paper tackled texture characterization in images by proposing a bio-inspired descriptor based on biodiversity and taxonomic measures, achieving advantages over existing texture descriptors and deep methods on natural and histopathological image datasets.
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.