A Novel Adaptively Binarizing Magnitude Vector Method in Local Binary Pattern Based Framework for Texture Classification

Local Binary Pattern (LBP) based framework only uses a scalar threshold to binarize all magnitude vectors in P different directions around each center pixel of a texture image. Hence, the original LBP-based framework, in fact, can not precisely extract different magnitude features in P different dir...

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Veröffentlicht in:IEEE signal processing letters 2022, Vol.29, p.852-856
Hauptverfasser: Hu, Shiqi, Pan, Zhibin, Dong, Jing, Ren, Xincheng
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description Local Binary Pattern (LBP) based framework only uses a scalar threshold to binarize all magnitude vectors in P different directions around each center pixel of a texture image. Hence, the original LBP-based framework, in fact, can not precisely extract different magnitude features in P different directions around each center pixel. Furthermore, the value of magnitude vectors can have dramatic changes from coarse areas to flat areas in the same texture image. Therefore, using a scalar threshold calculated from whole texture image can not precisely binarize all magnitude vectors in coarse areas and flat areas simultaneously. To overcome these two drawbacks, we propose a novel adaptively binarizing magnitude vector (ABMV) method. Firstly, we adaptively calculate the average vector threshold \boldsymbol{\vec{t}_{P}} with P different directional values of all magnitude vectors to replace the scalar threshold t to binarize the magnitude vectors. The proposed ABMV method can more precisely extract the different magnitude features in P different directions around each center pixel. Secondly, we divide the original texture image into smaller sub-images and adaptively extract their average vector threshold from each sub-image separately. Because the correlation of the pixels in the same sub-image is stronger than that in a whole texture image, the ABMV method can more precisely extract different magnitude features from either coarse areas or flat areas. Finally, we introduce the proposed ABMV method into LBP-based framework. Extensive experiments are conducted on five representative texture databases: Outex, UIUC, CUReT, XU_HR and ALOT database. After introducing the ABMV method into CLBP, CLBC, BRINT and CJLBP, the classification accuracy and the robustness to noise of these methods can be significantly improved.
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Hence, the original LBP-based framework, in fact, can not precisely extract different magnitude features in P different directions around each center pixel. Furthermore, the value of magnitude vectors can have dramatic changes from coarse areas to flat areas in the same texture image. Therefore, using a scalar threshold calculated from whole texture image can not precisely binarize all magnitude vectors in coarse areas and flat areas simultaneously. To overcome these two drawbacks, we propose a novel adaptively binarizing magnitude vector (ABMV) method. Firstly, we adaptively calculate the average vector threshold &lt;inline-formula&gt;&lt;tex-math notation="LaTeX"&gt;\boldsymbol{\vec{t}_{P}}&lt;/tex-math&gt;&lt;/inline-formula&gt; with P different directional values of all magnitude vectors to replace the scalar threshold t to binarize the magnitude vectors. The proposed ABMV method can more precisely extract the different magnitude features in P different directions around each center pixel. Secondly, we divide the original texture image into smaller sub-images and adaptively extract their average vector threshold from each sub-image separately. Because the correlation of the pixels in the same sub-image is stronger than that in a whole texture image, the ABMV method can more precisely extract different magnitude features from either coarse areas or flat areas. Finally, we introduce the proposed ABMV method into LBP-based framework. Extensive experiments are conducted on five representative texture databases: Outex, UIUC, CUReT, XU_HR and ALOT database. 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(IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><orcidid>https://orcid.org/0000-0002-4695-311X</orcidid></search><sort><creationdate>2022</creationdate><title>A Novel Adaptively Binarizing Magnitude Vector Method in Local Binary Pattern Based Framework for Texture Classification</title><author>Hu, Shiqi ; Pan, Zhibin ; Dong, Jing ; Ren, Xincheng</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c291t-1a7f643f1afd7f6c889a7c4ff4afa290198d59197a9e4c62da7edd45826b17193</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>adaptively binarizing magnitude vector (ABMV)</topic><topic>average vector threshold</topic><topic>Classification</topic><topic>Correlation</topic><topic>Data mining</topic><topic>Electronic mail</topic><topic>Feature extraction</topic><topic>Local binary pattern (LBP)</topic><topic>Mathematical analysis</topic><topic>Pixels</topic><topic>Robustness</topic><topic>Texture</topic><topic>texture classification</topic><topic>threshold</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Hu, Shiqi</creatorcontrib><creatorcontrib>Pan, Zhibin</creatorcontrib><creatorcontrib>Dong, Jing</creatorcontrib><creatorcontrib>Ren, Xincheng</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005–Present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics &amp; Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts – Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>IEEE signal processing letters</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Hu, Shiqi</au><au>Pan, Zhibin</au><au>Dong, Jing</au><au>Ren, Xincheng</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Novel Adaptively Binarizing Magnitude Vector Method in Local Binary Pattern Based Framework for Texture Classification</atitle><jtitle>IEEE signal processing letters</jtitle><stitle>LSP</stitle><date>2022</date><risdate>2022</risdate><volume>29</volume><spage>852</spage><epage>856</epage><pages>852-856</pages><issn>1070-9908</issn><eissn>1558-2361</eissn><coden>ISPLEM</coden><abstract>Local Binary Pattern (LBP) based framework only uses a scalar threshold to binarize all magnitude vectors in P different directions around each center pixel of a texture image. Hence, the original LBP-based framework, in fact, can not precisely extract different magnitude features in P different directions around each center pixel. Furthermore, the value of magnitude vectors can have dramatic changes from coarse areas to flat areas in the same texture image. Therefore, using a scalar threshold calculated from whole texture image can not precisely binarize all magnitude vectors in coarse areas and flat areas simultaneously. To overcome these two drawbacks, we propose a novel adaptively binarizing magnitude vector (ABMV) method. Firstly, we adaptively calculate the average vector threshold &lt;inline-formula&gt;&lt;tex-math notation="LaTeX"&gt;\boldsymbol{\vec{t}_{P}}&lt;/tex-math&gt;&lt;/inline-formula&gt; with P different directional values of all magnitude vectors to replace the scalar threshold t to binarize the magnitude vectors. The proposed ABMV method can more precisely extract the different magnitude features in P different directions around each center pixel. Secondly, we divide the original texture image into smaller sub-images and adaptively extract their average vector threshold from each sub-image separately. Because the correlation of the pixels in the same sub-image is stronger than that in a whole texture image, the ABMV method can more precisely extract different magnitude features from either coarse areas or flat areas. Finally, we introduce the proposed ABMV method into LBP-based framework. Extensive experiments are conducted on five representative texture databases: Outex, UIUC, CUReT, XU_HR and ALOT database. After introducing the ABMV method into CLBP, CLBC, BRINT and CJLBP, the classification accuracy and the robustness to noise of these methods can be significantly improved.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/LSP.2022.3158199</doi><tpages>5</tpages><orcidid>https://orcid.org/0000-0002-4695-311X</orcidid></addata></record>
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subjects adaptively binarizing magnitude vector (ABMV)
average vector threshold
Classification
Correlation
Data mining
Electronic mail
Feature extraction
Local binary pattern (LBP)
Mathematical analysis
Pixels
Robustness
Texture
texture classification
threshold
title A Novel Adaptively Binarizing Magnitude Vector Method in Local Binary Pattern Based Framework for Texture Classification
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