E-BiT: Extended bio-inspired texture descriptor for 2D texture analysis and characterization

Ataky, Steve Tsham Mpinda et Lameiras Koerich, Alessandro. 2023. « E-BiT: Extended bio-inspired texture descriptor for 2D texture analysis and characterization ». Electronics, vol. 12, nº 9.

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Résumé

This paper presents an extended bio-inspired texture (E-BiT) descriptor for image texture characterization. The E-BiT descriptor combines global ecological concepts of species diversity, evenness, richness, and taxonomic indexes to effectively capture texture patterns at local and global levels while maintaining invariance to scale, translation, and permutation. First, we pre-processed the images by normalizing and applying geometric transformations to assess the invariance properties of the proposed descriptor. Next, we assessed the performance of the proposed E-BiT descriptor on four datasets, including histopathological images and natural texture images. Finally, we compared it with the original BiT descriptor and other texture descriptors, such as Haralick, GLCM, and LBP. The E-BiT descriptor achieved state-of-the-art texture classification performance, with accuracy improvements ranging from 0.12% to 20% over other descriptors. In addition, the E-BiT descriptor demonstrated its generic nature by performing well in both natural and histopathologic images. Future work could examine the E-BiT descriptor’s behavior at different spatial scales and resolutions to optimize texture property extraction and improve performance.

Type de document:	Article publié dans une revue, révisé par les pairs
Professeur:	Professeur Lameiras Koerich, Alessandro
Affiliation:	Génie logiciel et des technologies de l'information
Date de dépôt:	30 mai 2023 20:35
Dernière modification:	31 mai 2023 16:12
URI:	https://espace2.etsmtl.ca/id/eprint/26457

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