Performance Evaluation of Different Local Binary Operators for Texture Classification

Local Binary Patterns (LBP) have brightened up as one of the most eminent and widely studied texture descriptors. The LBP descriptor 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 perfo...

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Main Authors:	Shamaileh, Abeer, Rassem, Taha H., Liew, Siau-Chuin, Al Sayaydeh, Osama Nayel
Format:	Article
Language:	English
Published:	The Science and Information (SAI) Organization Limited 2019
Subjects:	QA76 Computer software
Online Access:	http://umpir.ump.edu.my/id/eprint/24773/ http://umpir.ump.edu.my/id/eprint/24773/ http://umpir.ump.edu.my/id/eprint/24773/1/Performance%20Evaluation%20of%20Different%20Local%20Binary%20Operators%20%20for%20Texture%20Classification.pdf

id	ump-24773
recordtype	eprints
spelling	ump-247732019-05-29T04:49:57Z http://umpir.ump.edu.my/id/eprint/24773/ Performance Evaluation of Different Local Binary Operators for Texture Classification Shamaileh, Abeer Rassem, Taha H. Liew, Siau-Chuin Al Sayaydeh, Osama Nayel QA76 Computer software Local Binary Patterns (LBP) have brightened up as one of the most eminent and widely studied texture descriptors. The LBP descriptor 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. Several forms of LBP have been suggested to increase its discriminative ability during texture classification and to improve its robustness to noise. Since 2002, different texture descriptors had been proposed. These texture descriptors were inspired by LBP and proposed to overcome its limitations. Examples of these texture descriptors are 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). Due to the importance of texture descriptors in image classification, the performance of different texture descriptors is studied and investigated for image texture classification in this paper. This study also strived to improve the role of image texture information in classification processes. Different experiments were conducted using two benchmark texture datasets - CuRTex and OuTex. The experimental results showed that the WCLTP outperformed the remaining texture descriptors. The WCLTP achieved 99.35% and 96.89% classification performance accuracy with CuRTex and OuTRex, respectively. The Science and Information (SAI) Organization Limited 2019 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/24773/1/Performance%20Evaluation%20of%20Different%20Local%20Binary%20Operators%20%20for%20Texture%20Classification.pdf Shamaileh, Abeer and Rassem, Taha H. and Liew, Siau-Chuin and Al Sayaydeh, Osama Nayel (2019) Performance Evaluation of Different Local Binary Operators for Texture Classification. International Journal of Advanced Computer Science and Applications (IJACSA). ISSN 2156-5570 (Online) (In Press) https://thesai.org/Publications/IJACSA
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	QA76 Computer software
spellingShingle	QA76 Computer software Shamaileh, Abeer Rassem, Taha H. Liew, Siau-Chuin Al Sayaydeh, Osama Nayel Performance Evaluation of Different Local Binary Operators for Texture Classification
description	Local Binary Patterns (LBP) have brightened up as one of the most eminent and widely studied texture descriptors. The LBP descriptor 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. Several forms of LBP have been suggested to increase its discriminative ability during texture classification and to improve its robustness to noise. Since 2002, different texture descriptors had been proposed. These texture descriptors were inspired by LBP and proposed to overcome its limitations. Examples of these texture descriptors are 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). Due to the importance of texture descriptors in image classification, the performance of different texture descriptors is studied and investigated for image texture classification in this paper. This study also strived to improve the role of image texture information in classification processes. Different experiments were conducted using two benchmark texture datasets - CuRTex and OuTex. The experimental results showed that the WCLTP outperformed the remaining texture descriptors. The WCLTP achieved 99.35% and 96.89% classification performance accuracy with CuRTex and OuTRex, 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	Performance Evaluation of Different Local Binary Operators for Texture Classification
title_short	Performance Evaluation of Different Local Binary Operators for Texture Classification
title_full	Performance Evaluation of Different Local Binary Operators for Texture Classification
title_fullStr	Performance Evaluation of Different Local Binary Operators for Texture Classification
title_full_unstemmed	Performance Evaluation of Different Local Binary Operators for Texture Classification
title_sort	performance evaluation of different local binary operators for texture classification
publisher	The Science and Information (SAI) Organization Limited
publishDate	2019
url	http://umpir.ump.edu.my/id/eprint/24773/ http://umpir.ump.edu.my/id/eprint/24773/ http://umpir.ump.edu.my/id/eprint/24773/1/Performance%20Evaluation%20of%20Different%20Local%20Binary%20Operators%20%20for%20Texture%20Classification.pdf
first_indexed	2023-09-18T22:37:41Z
last_indexed	2023-09-18T22:37:41Z
_version_	1777416705933312000

Performance Evaluation of Different Local Binary Operators for Texture Classification

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