Medical, Scene And Event Image Category Recognition Using Completed Local Ternary Patterns (CLTP)

Many of texture descriptors are proposed based on the Local Binary Pattern (LBP) and have been achieved remarkable texture classification accuracy such as Completed LBP (CLBP) and Completed Local Binary Count (CLBC). However, the LBP suffers from two weaknesses where: 1) it is sensitive to noise and...

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Main Authors:	Rassem, Taha H., Bee, Ee Khoo, Mohammed, Mohammed Falah, Makbol, Nasrin M.
Format:	Article
Language:	English
Published:	Universiti Malaya 2017
Subjects:	QA75 Electronic computers. Computer science QA76 Computer software
Online Access:	http://umpir.ump.edu.my/id/eprint/18621/ http://umpir.ump.edu.my/id/eprint/18621/ http://umpir.ump.edu.my/id/eprint/18621/2/MJCS_Final.pdf

id	ump-18621
recordtype	eprints
spelling	ump-186212017-10-12T08:20:14Z http://umpir.ump.edu.my/id/eprint/18621/ Medical, Scene And Event Image Category Recognition Using Completed Local Ternary Patterns (CLTP) Rassem, Taha H. Bee, Ee Khoo Mohammed, Mohammed Falah Makbol, Nasrin M. QA75 Electronic computers. Computer science QA76 Computer software Many of texture descriptors are proposed based on the Local Binary Pattern (LBP) and have been achieved remarkable texture classification accuracy such as Completed LBP (CLBP) and Completed Local Binary Count (CLBC). However, the LBP suffers from two weaknesses where: 1) it is sensitive to noise and; 2) it sometimes classify two or more different patterns falsely to the same class. To overcome the LBP weaknesses, we propose a new texture descriptor which is defined as Completed Local Ternary Pattern (CLTP). The CLTP was used for rotation invariant texture classification. It demonstrates superior texture classification accuracy as compared to CLBP and CLBC descriptors. This is because, the CLTP is more robust to noise and has a high discriminating property that achieves impressive classification accuracy rates. In this paper, two types of experiments are carried out. In the first experiment, different amount of additive Gaussian noise is added to the TC10 Outex texture data set to investigate and prove the robustness of the CLTP against the noise. For the second experiment, the performance of CLTP for image category recognition is studied and investigated. A variety of image datasets are used in the experiments such as scene data set (e.g., Oliva and Torralba datasets (OT8)), Event sport datasets, 2D HeLa medical images, and our new scene data set, defined as USM scene data set. The experimental results proved the superiority of the CLTP descriptor over the original LBP, and different new texture descriptors such as CLBP in the image category recognition, as well as the robustness against the noise. In 2D HeLa medical images, the proposed CLTP has achieved the highest state of the art classification rate reaching 95.62%. Universiti Malaya 2017-09-23 Article PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/18621/2/MJCS_Final.pdf Rassem, Taha H. and Bee, Ee Khoo and Mohammed, Mohammed Falah and Makbol, Nasrin M. (2017) Medical, Scene And Event Image Category Recognition Using Completed Local Ternary Patterns (CLTP). Malaysian Journal of Computer Science, 30 (3). pp. 200-218. ISSN 0127-9084 https://ejournal.um.edu.my/index.php/MJCS/article/view/7042/
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	QA75 Electronic computers. Computer science QA76 Computer software
spellingShingle	QA75 Electronic computers. Computer science QA76 Computer software Rassem, Taha H. Bee, Ee Khoo Mohammed, Mohammed Falah Makbol, Nasrin M. Medical, Scene And Event Image Category Recognition Using Completed Local Ternary Patterns (CLTP)
description	Many of texture descriptors are proposed based on the Local Binary Pattern (LBP) and have been achieved remarkable texture classification accuracy such as Completed LBP (CLBP) and Completed Local Binary Count (CLBC). However, the LBP suffers from two weaknesses where: 1) it is sensitive to noise and; 2) it sometimes classify two or more different patterns falsely to the same class. To overcome the LBP weaknesses, we propose a new texture descriptor which is defined as Completed Local Ternary Pattern (CLTP). The CLTP was used for rotation invariant texture classification. It demonstrates superior texture classification accuracy as compared to CLBP and CLBC descriptors. This is because, the CLTP is more robust to noise and has a high discriminating property that achieves impressive classification accuracy rates. In this paper, two types of experiments are carried out. In the first experiment, different amount of additive Gaussian noise is added to the TC10 Outex texture data set to investigate and prove the robustness of the CLTP against the noise. For the second experiment, the performance of CLTP for image category recognition is studied and investigated. A variety of image datasets are used in the experiments such as scene data set (e.g., Oliva and Torralba datasets (OT8)), Event sport datasets, 2D HeLa medical images, and our new scene data set, defined as USM scene data set. The experimental results proved the superiority of the CLTP descriptor over the original LBP, and different new texture descriptors such as CLBP in the image category recognition, as well as the robustness against the noise. In 2D HeLa medical images, the proposed CLTP has achieved the highest state of the art classification rate reaching 95.62%.
format	Article
author	Rassem, Taha H. Bee, Ee Khoo Mohammed, Mohammed Falah Makbol, Nasrin M.
author_facet	Rassem, Taha H. Bee, Ee Khoo Mohammed, Mohammed Falah Makbol, Nasrin M.
author_sort	Rassem, Taha H.
title	Medical, Scene And Event Image Category Recognition Using Completed Local Ternary Patterns (CLTP)
title_short	Medical, Scene And Event Image Category Recognition Using Completed Local Ternary Patterns (CLTP)
title_full	Medical, Scene And Event Image Category Recognition Using Completed Local Ternary Patterns (CLTP)
title_fullStr	Medical, Scene And Event Image Category Recognition Using Completed Local Ternary Patterns (CLTP)
title_full_unstemmed	Medical, Scene And Event Image Category Recognition Using Completed Local Ternary Patterns (CLTP)
title_sort	medical, scene and event image category recognition using completed local ternary patterns (cltp)
publisher	Universiti Malaya
publishDate	2017
url	http://umpir.ump.edu.my/id/eprint/18621/ http://umpir.ump.edu.my/id/eprint/18621/ http://umpir.ump.edu.my/id/eprint/18621/2/MJCS_Final.pdf
first_indexed	2023-09-18T22:26:29Z
last_indexed	2023-09-18T22:26:29Z
_version_	1777416000550993920

Medical, Scene And Event Image Category Recognition Using Completed Local Ternary Patterns (CLTP)

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