Wavelet Completed Local Ternary Pattern (WCLTP) for Texture Image Classification

In this paper, a new texture descriptor inspired from Completed Local Ternary Pattern (CLTP) is proposed and investigated for texture image classification task. A wavelet-CLTP (WCLTP) is proposed by integrating the CLTP with the redundant discrete wavelet transform (RDWT). Firstly, the images are de...

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Main Authors:	Shamaileh, Abeer, Rassem, Taha H., Ahmed, Ibrahim Abdulrab, Alalayah, Khaled M.
Format:	Article
Language:	English
Published:	American Scientific Publisher 2018
Subjects:	QA76 Computer software
Online Access:	http://umpir.ump.edu.my/id/eprint/20127/ http://umpir.ump.edu.my/id/eprint/20127/ http://umpir.ump.edu.my/id/eprint/20127/ http://umpir.ump.edu.my/id/eprint/20127/6/Wavelet%20Completed%20Local%20Ternary%20Pattern%20%28WCLTP%29.pdf

id	ump-20127
recordtype	eprints
spelling	ump-201272018-11-22T05:03:50Z http://umpir.ump.edu.my/id/eprint/20127/ Wavelet Completed Local Ternary Pattern (WCLTP) for Texture Image Classification Shamaileh, Abeer Rassem, Taha H. Ahmed, Ibrahim Abdulrab Alalayah, Khaled M. QA76 Computer software In this paper, a new texture descriptor inspired from Completed Local Ternary Pattern (CLTP) is proposed and investigated for texture image classification task. A wavelet-CLTP (WCLTP) is proposed by integrating the CLTP with the redundant discrete wavelet transform (RDWT). Firstly, the images are decomposed using RDWT into four sub-bands. Then, the CLTP are extracted from the LL sub-bands coefficients of the image. The RDWT is selected due to its advantages. Unlike the other wavelet transform, the RDWT decompose the images into the same size sub-bands. So, the important textures in the image will be at the same spatial location in each sub-band. As a result, more accurate capturing of the local texture within RDWT domain can be done and the exact measure of local texture can be used. The proposed WCLTP is evaluated for rotation invariant texture classification task. The experimental results using CURTex and Outex texture databases show that the proposed WCLTP outperformed the CLBP and CLBC descriptors and achieved an impressive classification accuracy. Furthermore, the WCLTP outperformed the CLTP in Outex and many cases in the CURTex databases. American Scientific Publisher 2018-11 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/20127/6/Wavelet%20Completed%20Local%20Ternary%20Pattern%20%28WCLTP%29.pdf Shamaileh, Abeer and Rassem, Taha H. and Ahmed, Ibrahim Abdulrab and Alalayah, Khaled M. (2018) Wavelet Completed Local Ternary Pattern (WCLTP) for Texture Image Classification. Advanced Science Letters, 24 (10). pp. 7675-7681. ISSN 1936-6612 https://doi.org/10.1166/asl.2018.12998 doi: 10.1166/asl.2018.12998
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. Ahmed, Ibrahim Abdulrab Alalayah, Khaled M. Wavelet Completed Local Ternary Pattern (WCLTP) for Texture Image Classification
description	In this paper, a new texture descriptor inspired from Completed Local Ternary Pattern (CLTP) is proposed and investigated for texture image classification task. A wavelet-CLTP (WCLTP) is proposed by integrating the CLTP with the redundant discrete wavelet transform (RDWT). Firstly, the images are decomposed using RDWT into four sub-bands. Then, the CLTP are extracted from the LL sub-bands coefficients of the image. The RDWT is selected due to its advantages. Unlike the other wavelet transform, the RDWT decompose the images into the same size sub-bands. So, the important textures in the image will be at the same spatial location in each sub-band. As a result, more accurate capturing of the local texture within RDWT domain can be done and the exact measure of local texture can be used. The proposed WCLTP is evaluated for rotation invariant texture classification task. The experimental results using CURTex and Outex texture databases show that the proposed WCLTP outperformed the CLBP and CLBC descriptors and achieved an impressive classification accuracy. Furthermore, the WCLTP outperformed the CLTP in Outex and many cases in the CURTex databases.
format	Article
author	Shamaileh, Abeer Rassem, Taha H. Ahmed, Ibrahim Abdulrab Alalayah, Khaled M.
author_facet	Shamaileh, Abeer Rassem, Taha H. Ahmed, Ibrahim Abdulrab Alalayah, Khaled M.
author_sort	Shamaileh, Abeer
title	Wavelet Completed Local Ternary Pattern (WCLTP) for Texture Image Classification
title_short	Wavelet Completed Local Ternary Pattern (WCLTP) for Texture Image Classification
title_full	Wavelet Completed Local Ternary Pattern (WCLTP) for Texture Image Classification
title_fullStr	Wavelet Completed Local Ternary Pattern (WCLTP) for Texture Image Classification
title_full_unstemmed	Wavelet Completed Local Ternary Pattern (WCLTP) for Texture Image Classification
title_sort	wavelet completed local ternary pattern (wcltp) for texture image classification
publisher	American Scientific Publisher
publishDate	2018
url	http://umpir.ump.edu.my/id/eprint/20127/ http://umpir.ump.edu.my/id/eprint/20127/ http://umpir.ump.edu.my/id/eprint/20127/ http://umpir.ump.edu.my/id/eprint/20127/6/Wavelet%20Completed%20Local%20Ternary%20Pattern%20%28WCLTP%29.pdf
first_indexed	2023-09-18T22:28:51Z
last_indexed	2023-09-18T22:28:51Z
_version_	1777416149181399040

Wavelet Completed Local Ternary Pattern (WCLTP) for Texture Image Classification

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