文章基本信息

标题：A Local Binary Pattern-Based Method for Color and Multicomponent Texture Analysis
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作者：Yao Taky Alvarez Kossonou ; Alain Clément ; Bouchta Sahraoui 等
期刊名称：Journal of Signal and Information Processing
印刷版ISSN：2159-4465
电子版ISSN：2159-4481
出版年度：2020
卷号：11
期号：3
页码：58-73
DOI：10.4236/jsip.2020.113004
出版社：Scientific Research Publishing
摘要：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.
关键词：Multispectral Images;Local Binary Patterns (LBP);Texture Analysis;Rotation Invariance;Illumination Variation;Blurring Invariance