Texture Image Classification Using Wavelet Completed Local Binary Pattern Descriptor (WCLBP)

In this paper, a new texture descriptor inspired from completed local binary pattern (CLBP) is proposed and investigated for texture image classification task. A waveletCLBP (WCLBP) is proposed by integrating the CLBP with the redundant discrete wavelet transform (RDWT). Firstly, the images are deco...

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Main Authors:	Rassem, Taha H., Al-Sewari, Abdul Rahman Ahmed Mohammed, Nasrin, M. Makbol
Format:	Conference or Workshop Item
Language:	English
Published:	IEEE 2017
Subjects:	QA76 Computer software
Online Access:	http://umpir.ump.edu.my/id/eprint/18914/ http://umpir.ump.edu.my/id/eprint/18914/ http://umpir.ump.edu.my/id/eprint/18914/1/Texture%20Image%20Classification%20Using%20Wavelet%20Completed%20Local%20Binary%20Pattern%20Descriptor1.pdf

id	ump-18914
recordtype	eprints
spelling	ump-189142019-10-04T03:00:01Z http://umpir.ump.edu.my/id/eprint/18914/ Texture Image Classification Using Wavelet Completed Local Binary Pattern Descriptor (WCLBP) Rassem, Taha H. Al-Sewari, Abdul Rahman Ahmed Mohammed Nasrin, M. Makbol QA76 Computer software In this paper, a new texture descriptor inspired from completed local binary pattern (CLBP) is proposed and investigated for texture image classification task. A waveletCLBP (WCLBP) is proposed by integrating the CLBP with the redundant discrete wavelet transform (RDWT). Firstly, the images are decomposed using RDWT into four sub-bands. Then, the CLBP 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 WCLBP is evaluated for rotation invariant texture classification task. The experimental results using CURTex and OuTex texture databases show that the proposed WCLBP outperformed the CLBP and CLBC descriptors and achieved an impressive classification accuracy. IEEE 2017 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/18914/1/Texture%20Image%20Classification%20Using%20Wavelet%20Completed%20Local%20Binary%20Pattern%20Descriptor1.pdf Rassem, Taha H. and Al-Sewari, Abdul Rahman Ahmed Mohammed and Nasrin, M. Makbol (2017) Texture Image Classification Using Wavelet Completed Local Binary Pattern Descriptor (WCLBP). In: IEEE The 7th Conference On Innovative Computing Technology (INTECH 2017), 15-19 August 2017 , University of Bedfordshire, Luton, UK. pp. 15-20.. ISBN 978-150903988-3 (Unpublished) https://doi.org/10.1109/INTECH.2017.8102416
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 Rassem, Taha H. Al-Sewari, Abdul Rahman Ahmed Mohammed Nasrin, M. Makbol Texture Image Classification Using Wavelet Completed Local Binary Pattern Descriptor (WCLBP)
description	In this paper, a new texture descriptor inspired from completed local binary pattern (CLBP) is proposed and investigated for texture image classification task. A waveletCLBP (WCLBP) is proposed by integrating the CLBP with the redundant discrete wavelet transform (RDWT). Firstly, the images are decomposed using RDWT into four sub-bands. Then, the CLBP 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 WCLBP is evaluated for rotation invariant texture classification task. The experimental results using CURTex and OuTex texture databases show that the proposed WCLBP outperformed the CLBP and CLBC descriptors and achieved an impressive classification accuracy.
format	Conference or Workshop Item
author	Rassem, Taha H. Al-Sewari, Abdul Rahman Ahmed Mohammed Nasrin, M. Makbol
author_facet	Rassem, Taha H. Al-Sewari, Abdul Rahman Ahmed Mohammed Nasrin, M. Makbol
author_sort	Rassem, Taha H.
title	Texture Image Classification Using Wavelet Completed Local Binary Pattern Descriptor (WCLBP)
title_short	Texture Image Classification Using Wavelet Completed Local Binary Pattern Descriptor (WCLBP)
title_full	Texture Image Classification Using Wavelet Completed Local Binary Pattern Descriptor (WCLBP)
title_fullStr	Texture Image Classification Using Wavelet Completed Local Binary Pattern Descriptor (WCLBP)
title_full_unstemmed	Texture Image Classification Using Wavelet Completed Local Binary Pattern Descriptor (WCLBP)
title_sort	texture image classification using wavelet completed local binary pattern descriptor (wclbp)
publisher	IEEE
publishDate	2017
url	http://umpir.ump.edu.my/id/eprint/18914/ http://umpir.ump.edu.my/id/eprint/18914/ http://umpir.ump.edu.my/id/eprint/18914/1/Texture%20Image%20Classification%20Using%20Wavelet%20Completed%20Local%20Binary%20Pattern%20Descriptor1.pdf
first_indexed	2023-09-18T22:27:01Z
last_indexed	2023-09-18T22:27:01Z
_version_	1777416034373861376

Texture Image Classification Using Wavelet Completed Local Binary Pattern Descriptor (WCLBP)

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