A Local Binary Pattern-Based Method for Color and Multicomponent Texture Analysis - Journal of Signal and Information Processing

JSIP > Vol.11 No.3, August 2020

Journal of Signal and Information Processing

Volume 11, Issue 3 (August 2020)

ISSN Print: 2159-4465 ISSN Online: 2159-4481

Google-based Impact Factor: 1.19 Citations

A Local Binary Pattern-Based Method for Color and Multicomponent Texture Analysis ()

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DOI: 10.4236/jsip.2020.113004 564 Downloads 1,927 Views Citations

Author(s)

Yao Taky Alvarez Kossonou^1,2, Alain Clément², Bouchta Sahraoui³, Jérémie Zoueu¹

Affiliation(s)

¹Laboratoire d’Instrumentation, Image et Spectroscopie (L2IS), Institut National Polytechnique-Felix Houphouët Boigny, Yamoussoukro, Côte d’Ivoire.
²Laboratoire Angevin de Recherche en Ingénierie des Systèmes (LARIS), Université d’Angers, Institut Universitaire de Technologie, 4 Bd Lavoisier, Angers Cedex, France.
³Institut des Sciences et Technologies Moléculaires d’Angers (MOLTECH ANJOU) UMR CNRS 6200, Université d’Angers, 2 Bd Lavoisier, Angers Cedex 2, France.

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.

KEYWORDS

Multispectral Images, Local Binary Patterns (LBP), Texture Analysis, Rotation Invariance, Illumination Variation, Blurring Invariance

Share and Cite:

Kossonou, Y. , Clément, A. , Sahraoui, B. and Zoueu, J. (2020) A Local Binary Pattern-Based Method for Color and Multicomponent Texture Analysis. Journal of Signal and Information Processing, 11, 58-73. doi: 10.4236/jsip.2020.113004.

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