Different local binary operators for texture classification: a comparative study

Local Binary Patterns (LBP) have brightened up as one of the most eminent and widely studied texture descriptors. LBP has gained high acceptance due to its simplicity, high distinguishing power, and flexibility. As such, it has been deployed in several applications where it has performed well. This...

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Main Authors:	Shamaileh, Abeer, Rassem, Taha H., Liew, Siau-Chuin, Al Sayaydeh, Osama Nayel
Format:	Article
Language:	English English
Published:	The World Academy of Research in Science and Engineering 2019
Subjects:	QA75 Electronic computers. Computer science
Online Access:	http://umpir.ump.edu.my/id/eprint/25582/ http://umpir.ump.edu.my/id/eprint/25582/ http://umpir.ump.edu.my/id/eprint/25582/1/Performance%20Evaluation%20of%20Different%20Local%20Binary%20Operators%20%20for%20Texture%20Classification.pdf http://umpir.ump.edu.my/id/eprint/25582/7/Performance%20Evaluation%20of%20Different%20Local%20Binary%20Operators%20for.pdf

id	ump-25582
recordtype	eprints
spelling	ump-255822019-11-20T08:12:19Z http://umpir.ump.edu.my/id/eprint/25582/ Different local binary operators for texture classification: a comparative study Shamaileh, Abeer Rassem, Taha H. Liew, Siau-Chuin Al Sayaydeh, Osama Nayel QA75 Electronic computers. Computer science Local Binary Patterns (LBP) have brightened up as one of the most eminent and widely studied texture descriptors. LBP has gained high acceptance due to its simplicity, high distinguishing power, and flexibility. As such, it has been deployed in several applications where it has performed well. This is why LBP is the basis for a new research direction. However, LBP has limitations that may affect its accuracy. Therefore, many descriptors based on LBP have been proposed to overcome its limitations and enhance its accuracy, such as Local Ternary Pattern (LTP), Completed Local Binary Pattern (CLBP), Completed Local Binary Count (CLBC), Completed Local Ternary Pattern (CLTP), and Wavelet Completed Local Ternary Pattern (WCLTP). This paper is focused to provide a comparative analysis by studying and evaluating the performance of LBP descriptor and five of its variants using three well-known benchmark texture datasets. Furthermore, this study also seeks to improve the role of image texture information in classification processes. Different experiments were conducted using three benchmark texture datasets which are CUReT, OuTeX and UIUC. The experimental results showed that WCLTP outperformed other texture descriptors and achieved the highest classification accuracy in all experiments. WCLTP achieved 99.35%, 96.57% and 94.80% classification accuracy with CUReT and OuTeX and UIUC respectively. The World Academy of Research in Science and Engineering 2019-05 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/25582/1/Performance%20Evaluation%20of%20Different%20Local%20Binary%20Operators%20%20for%20Texture%20Classification.pdf pdf en http://umpir.ump.edu.my/id/eprint/25582/7/Performance%20Evaluation%20of%20Different%20Local%20Binary%20Operators%20for.pdf Shamaileh, Abeer and Rassem, Taha H. and Liew, Siau-Chuin and Al Sayaydeh, Osama Nayel (2019) Different local binary operators for texture classification: a comparative study. International Journal of Advanced Trends in Computer Science and Engineering, 8 (3). pp. 889-896. ISSN 2278 - 3091 http://doi.org/10.30534/ijatcse/2019/85832019
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English English
topic	QA75 Electronic computers. Computer science
spellingShingle	QA75 Electronic computers. Computer science Shamaileh, Abeer Rassem, Taha H. Liew, Siau-Chuin Al Sayaydeh, Osama Nayel Different local binary operators for texture classification: a comparative study
description	Local Binary Patterns (LBP) have brightened up as one of the most eminent and widely studied texture descriptors. LBP has gained high acceptance due to its simplicity, high distinguishing power, and flexibility. As such, it has been deployed in several applications where it has performed well. This is why LBP is the basis for a new research direction. However, LBP has limitations that may affect its accuracy. Therefore, many descriptors based on LBP have been proposed to overcome its limitations and enhance its accuracy, such as Local Ternary Pattern (LTP), Completed Local Binary Pattern (CLBP), Completed Local Binary Count (CLBC), Completed Local Ternary Pattern (CLTP), and Wavelet Completed Local Ternary Pattern (WCLTP). This paper is focused to provide a comparative analysis by studying and evaluating the performance of LBP descriptor and five of its variants using three well-known benchmark texture datasets. Furthermore, this study also seeks to improve the role of image texture information in classification processes. Different experiments were conducted using three benchmark texture datasets which are CUReT, OuTeX and UIUC. The experimental results showed that WCLTP outperformed other texture descriptors and achieved the highest classification accuracy in all experiments. WCLTP achieved 99.35%, 96.57% and 94.80% classification accuracy with CUReT and OuTeX and UIUC respectively.
format	Article
author	Shamaileh, Abeer Rassem, Taha H. Liew, Siau-Chuin Al Sayaydeh, Osama Nayel
author_facet	Shamaileh, Abeer Rassem, Taha H. Liew, Siau-Chuin Al Sayaydeh, Osama Nayel
author_sort	Shamaileh, Abeer
title	Different local binary operators for texture classification: a comparative study
title_short	Different local binary operators for texture classification: a comparative study
title_full	Different local binary operators for texture classification: a comparative study
title_fullStr	Different local binary operators for texture classification: a comparative study
title_full_unstemmed	Different local binary operators for texture classification: a comparative study
title_sort	different local binary operators for texture classification: a comparative study
publisher	The World Academy of Research in Science and Engineering
publishDate	2019
url	http://umpir.ump.edu.my/id/eprint/25582/ http://umpir.ump.edu.my/id/eprint/25582/ http://umpir.ump.edu.my/id/eprint/25582/1/Performance%20Evaluation%20of%20Different%20Local%20Binary%20Operators%20%20for%20Texture%20Classification.pdf http://umpir.ump.edu.my/id/eprint/25582/7/Performance%20Evaluation%20of%20Different%20Local%20Binary%20Operators%20for.pdf
first_indexed	2023-09-18T22:39:22Z
last_indexed	2023-09-18T22:39:22Z
_version_	1777416810844389376

Different local binary operators for texture classification: a comparative study

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