A Local Binary Pattern-Based Method for Color and Multicomponent Texture Analysis — Oak Academic Publishing
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A Local Binary Pattern-Based Method for Color and Multicomponent Texture Analysis
Laboratoire d’Instrumentation, Image et Spectroscopie (L2IS), Institut National Polytechnique-Felix Houphouët Boigny, Yamoussoukro, Côte d’Ivoire
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Laboratoire Angevin de Recherche en Ingénierie des Systèmes (LARIS), Université d’Angers, Institut Universitaire de Technologie, 4 Bd Lavoisier, Angers Cedex, France
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Institut des Sciences et Technologies Moléculaires d’Angers (MOLTECH ANJOU) UMR CNRS 6200, Université d’Angers, 2 Bd Lavoisier, Angers Cedex 2, France
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Laboratoire d’Instrumentation, Image et Spectroscopie (L2IS), Institut National Polytechnique-Felix Houphouët Boigny, Yamoussoukro, Côte d’Ivoire
1 Laboratoire d’Instrumentation, Image et Spectroscopie (L2IS), Institut National Polytechnique-Felix Houphouët Boigny, Yamoussoukro, Côte d’Ivoire
2 Laboratoire Angevin de Recherche en Ingénierie des Systèmes (LARIS), Université d’Angers, Institut Universitaire de Technologie, 4 Bd Lavoisier, Angers Cedex, France
3 Institut des Sciences et Technologies Moléculaires d’Angers (MOLTECH ANJOU) UMR CNRS 6200, Université d’Angers, 2 Bd Lavoisier, Angers Cedex 2, France
4 Laboratoire d’Instrumentation, Image et Spectroscopie (L2IS), Institut National Polytechnique-Felix Houphouët Boigny, Yamoussoukro, Côte d’Ivoire
Local Binary Patterns (LBPs) have been highly used in texture classification for their robustness, their ease of implementation an d 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 analysi s withou t 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 propos al 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.
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