Refined Color Texture Classification Using CNN and Local Binary Pattern

Representation and classification of color texture generate considerable interest within the field of computer vision. Texture classification is a difficult task that assigns unlabeled images or textures to the correct labeled class. Some key factors such as scaling and viewpoint variations and illu...

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Autores principales:	Khalid M. Hosny, Taher Magdy, Nabil A. Lashin, Kyriakos Apostolidis, George A. Papakostas
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Engineering (General). Civil engineering (General) TA1-2040 Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/1e344b15f049436c98140f36b328c7e6
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spelling	oai:doaj.org-article:1e344b15f049436c98140f36b328c7e62021-11-29T00:56:22ZRefined Color Texture Classification Using CNN and Local Binary Pattern1563-514710.1155/2021/5567489https://doaj.org/article/1e344b15f049436c98140f36b328c7e62021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/5567489https://doaj.org/toc/1563-5147Representation and classification of color texture generate considerable interest within the field of computer vision. Texture classification is a difficult task that assigns unlabeled images or textures to the correct labeled class. Some key factors such as scaling and viewpoint variations and illumination changes make this task challenging. In this paper, we present a new feature extraction technique for color texture classification and recognition. The presented approach aggregates the features extracted from local binary patterns (LBP) and convolution neural network (CNN) to provide discriminatory information, leading to better texture classification results. Almost all of the CNN model cases classify images based on global features that describe the image as a whole to generalize the entire object. LBP classifies images based on local features that describe the image’s key points (image patches). Our analysis shows that using LBP improves the classification task when compared to using CNN only. We test the proposed approach experimentally over three challenging color image datasets (ALOT, CBT, and Outex). The results demonstrated that our approach improved up to 25% in the classification accuracy over the traditional CNN models. We identify optimal combinations for each dataset and obtain high classification rates. The proposed approach is robust, stable, and discriminatory among the three datasets and has shown enhancement in classification and recognition compared to the state-of-the-art method.Khalid M. HosnyTaher MagdyNabil A. LashinKyriakos ApostolidisGeorge A. PapakostasHindawi LimitedarticleEngineering (General). Civil engineering (General)TA1-2040MathematicsQA1-939ENMathematical Problems in Engineering, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Engineering (General). Civil engineering (General) TA1-2040 Mathematics QA1-939
spellingShingle	Engineering (General). Civil engineering (General) TA1-2040 Mathematics QA1-939 Khalid M. Hosny Taher Magdy Nabil A. Lashin Kyriakos Apostolidis George A. Papakostas Refined Color Texture Classification Using CNN and Local Binary Pattern
description	Representation and classification of color texture generate considerable interest within the field of computer vision. Texture classification is a difficult task that assigns unlabeled images or textures to the correct labeled class. Some key factors such as scaling and viewpoint variations and illumination changes make this task challenging. In this paper, we present a new feature extraction technique for color texture classification and recognition. The presented approach aggregates the features extracted from local binary patterns (LBP) and convolution neural network (CNN) to provide discriminatory information, leading to better texture classification results. Almost all of the CNN model cases classify images based on global features that describe the image as a whole to generalize the entire object. LBP classifies images based on local features that describe the image’s key points (image patches). Our analysis shows that using LBP improves the classification task when compared to using CNN only. We test the proposed approach experimentally over three challenging color image datasets (ALOT, CBT, and Outex). The results demonstrated that our approach improved up to 25% in the classification accuracy over the traditional CNN models. We identify optimal combinations for each dataset and obtain high classification rates. The proposed approach is robust, stable, and discriminatory among the three datasets and has shown enhancement in classification and recognition compared to the state-of-the-art method.
format	article
author	Khalid M. Hosny Taher Magdy Nabil A. Lashin Kyriakos Apostolidis George A. Papakostas
author_facet	Khalid M. Hosny Taher Magdy Nabil A. Lashin Kyriakos Apostolidis George A. Papakostas
author_sort	Khalid M. Hosny
title	Refined Color Texture Classification Using CNN and Local Binary Pattern
title_short	Refined Color Texture Classification Using CNN and Local Binary Pattern
title_full	Refined Color Texture Classification Using CNN and Local Binary Pattern
title_fullStr	Refined Color Texture Classification Using CNN and Local Binary Pattern
title_full_unstemmed	Refined Color Texture Classification Using CNN and Local Binary Pattern
title_sort	refined color texture classification using cnn and local binary pattern
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/1e344b15f049436c98140f36b328c7e6
work_keys_str_mv	AT khalidmhosny refinedcolortextureclassificationusingcnnandlocalbinarypattern AT tahermagdy refinedcolortextureclassificationusingcnnandlocalbinarypattern AT nabilalashin refinedcolortextureclassificationusingcnnandlocalbinarypattern AT kyriakosapostolidis refinedcolortextureclassificationusingcnnandlocalbinarypattern AT georgeapapakostas refinedcolortextureclassificationusingcnnandlocalbinarypattern
_version_	1718407692776636416

Refined Color Texture Classification Using CNN and Local Binary Pattern

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