TITLE:
A Local Binary Pattern-Based Method for Color and Multicomponent Texture Analysis
AUTHORS:
Yao Taky Alvarez Kossonou, Alain Clément, Bouchta Sahraoui, Jérémie Zoueu
KEYWORDS:
Multispectral Images, Local Binary Patterns (LBP), Texture Analysis, Rotation Invariance, Illumination Variation, Blurring Invariance
JOURNAL NAME:
Journal of Signal and Information Processing,
Vol.11 No.3,
August
31,
2020
ABSTRACT: Local Binary Patterns (LBPs) have been highly used in texture
classification for their robustness, their
ease of implementation and their low computational cost. Initially designed
to deal with gray level images, several methods based on them in the literature
have been proposed for images having more than one spectral band. To achieve
it, whether assumption using color information or combining spectral band two
by two was done. Those methods use micro structures
as texture features. In this paper, our goal was to design texture features
which are relevant to color and multicomponent texture analysis without any assumption. Based on methods designed
for gray scale images, we find the combination of micro and macro structures
efficient for multispectral texture analysis. The experimentations were carried
out on color images from Outex databases and multicomponent images from red
blood cells captured using a multispectral microscope equipped with 13 LEDs
ranging from 375 nm to 940 nm. In all
achieved experimentations, our proposal presents the best classification
scores compared to common multicomponent LBP methods. 99.81%, 100.00%, 99.07% and 97.67% are maximum scores obtained with our strategy
respectively applied to images subject to rotation, blur, illumination
variation and the multicomponent ones.