Analysis of GLCM-feature-based dimensionality reduction and feature extraction methods for classifying fabric design patterns by using video data

Analysis of GLCM-feature-based dimensionality reduction and feature extraction methods for classifying fabric design patterns by using video data | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Method Article Analysis of GLCM-feature-based dimensionality reduction and feature extraction methods for classifying fabric design patterns by using video data Abdullah Al Mamun, Mohammad Abrar Uddin, Taeil Kim, Mahathir Mohammad Bappy This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5370165/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Manufacturing industries now leverage high-dimensional streaming video data from diverse sensors, represented as tensors (multidimensional arrays of channels × signals × time), for real-time monitoring, inspection, and quality control; however, this data often contains redundancy and captures only a subset of the complete dataset. Selecting effective dimensionality reduction and feature extraction methods for high-dimensional data structures remains challenging. To address these challenges, this paper presents a comparative framework for effective dimensionality reduction and feature extraction, utilizing supervised methods—Principal Component Analysis (PCA) and Independent Component Analysis (ICA)—alongside the unsupervised Multilinear-PCA (MPCA), which can more effectively handle multidimensional tensor structures compared to the 1-D or 2-D limitations of PCA and ICA. We evaluate this comparative framework for classifying fabric design patterns using high-dimensional video data captured from various fabric surface weave patterns. The videos are converted into sequential RGB frames and analyzed using the Gray-Level Co-occurrence Matrix (GLCM) for feature extraction, after which the dimensionality of the GLCM features is reduced with PCA, ICA, and MPCA, and the features are classified using supervised machine learning techniques for fabric design pattern recognition. MPCA achieves a 0.022% dimensionality reduction by extracting uniformly distributed features that effectively capture correlated fabric design patterns, unlike the less organized distributions from PCA and ICA. The fabric pattern classification accuracy achieved with MPCA, PCA, and ICA was 99.02%, 95.21%, and 92.68%, respectively. These results suggest that the proposed framework effectively facilitates dimensionality reduction and feature extraction in both supervised and unsupervised methods for high-dimensional video data. Analysis dimensionality reduction feature extraction pattern recognition surface texture features tensor decomposition Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5370165","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Method Article","associatedPublications":[],"authors":[{"id":372814778,"identity":"b313ce33-e705-42cd-b9d7-58ba60d54192","order_by":0,"name":"Abdullah Al Mamun","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAqUlEQVRIiWNgGAWjYPACGwjFQ4KWNNK1HCZBC/+0w0838+44n7id/QDjg7dtRGiRuJ1mdpv3zO3EnT0JzIZzidHCcDsBqKXtduKGAwls0rzEaJG/nf4NqOVc4obzD9h/E6XF4HYOyJYDiRtuJLAxE6XF8HZO2c25bcnGG248bJacc44ILXK307fdeNtmJ7vhfPLBD2/KiNCCBBgbSFM/CkbBKBgFowA3AABl8js0c6DZLwAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-9199-8974","institution":"University of North Alabama","correspondingAuthor":true,"prefix":"","firstName":"Abdullah","middleName":"Al","lastName":"Mamun","suffix":""},{"id":372814821,"identity":"2fd02761-7572-4775-ae5a-5925d9c9cc92","order_by":1,"name":"Mohammad Abrar Uddin","email":"","orcid":"","institution":"Baylor University","correspondingAuthor":false,"prefix":"","firstName":"Mohammad","middleName":"Abrar","lastName":"Uddin","suffix":""},{"id":372814928,"identity":"84cb1ecf-7857-4299-a8b3-1e020b251475","order_by":2,"name":"Taeil Kim","email":"","orcid":"","institution":"Baylor University","correspondingAuthor":false,"prefix":"","firstName":"Taeil","middleName":"","lastName":"Kim","suffix":""},{"id":372814943,"identity":"042cbbda-0447-4709-b268-d72c5fd714eb","order_by":3,"name":"Mahathir Mohammad Bappy","email":"","orcid":"","institution":"Louisiana State University","correspondingAuthor":false,"prefix":"","firstName":"Mahathir","middleName":"Mohammad","lastName":"Bappy","suffix":""}],"badges":[],"createdAt":"2024-11-01 03:09:22","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":true,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":true},"doi":"10.21203/rs.3.rs-5370165/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5370165/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":68132820,"identity":"b636efcd-5bda-490c-b654-e1f7136d5c03","added_by":"auto","created_at":"2024-11-04 02:43:00","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1611959,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscriptwithauthorsname.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5370165/v1_covered_fbd3ad1b-a10e-455f-95ea-373dea4738e6.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eAnalysis of GLCM-feature-based dimensionality reduction and feature extraction methods for classifying fabric design patterns by using video data\u003c/strong\u003e\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"dimensionality reduction, feature extraction, pattern recognition, surface texture features, tensor decomposition","lastPublishedDoi":"10.21203/rs.3.rs-5370165/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5370165/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eManufacturing industries now leverage high-dimensional streaming video data from diverse sensors, represented as tensors (multidimensional arrays of channels \u0026times; signals \u0026times; time), for real-time monitoring, inspection, and quality control; however, this data often contains redundancy and captures only a subset of the complete dataset. Selecting effective dimensionality reduction and feature extraction methods for high-dimensional data structures remains challenging. To address these challenges, this paper presents a comparative framework for effective dimensionality reduction and feature extraction, utilizing supervised methods\u0026mdash;Principal Component Analysis (PCA) and Independent Component Analysis (ICA)\u0026mdash;alongside the unsupervised Multilinear-PCA (MPCA), which can more effectively handle multidimensional tensor structures compared to the 1-D or 2-D limitations of PCA and ICA. We evaluate this comparative framework for classifying fabric design patterns using high-dimensional video data captured from various fabric surface weave patterns. The videos are converted into sequential RGB frames and analyzed using the Gray-Level Co-occurrence Matrix (GLCM) for feature extraction, after which the dimensionality of the GLCM features is reduced with PCA, ICA, and MPCA, and the features are classified using supervised machine learning techniques for fabric design pattern recognition. MPCA achieves a 0.022% dimensionality reduction by extracting uniformly distributed features that effectively capture correlated fabric design patterns, unlike the less organized distributions from PCA and ICA. The fabric pattern classification accuracy achieved with MPCA, PCA, and ICA was 99.02%, 95.21%, and 92.68%, respectively. 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