Enhanced Content-Based Image Retrieval through Integrated Local Average Binary Patterns and Joint Color Probabilities

Enhanced Content-Based Image Retrieval through Integrated Local Average Binary Patterns and Joint Color Probabilities | 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 Research Article Enhanced Content-Based Image Retrieval through Integrated Local Average Binary Patterns and Joint Color Probabilities Seyyed Ali Hosseini, AmirHossein Eshghi, Saba Mohammadi, Abdollah Zakeri This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5317346/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 Artificial intelligence techniques for image pattern recognition and retrieval are pivotal in various applications, particularly in medical imaging. Despite numerous existing methods, the process remains complex and computationally intensive. This study introduces an innovative approach to content-based image retrieval (CBIR) by integrating Local Average Binary Patterns (LABP) and the joint probability distribution of color channels. LABP extends the traditional Local Binary Pattern (LBP) by considering multiple layers of neighboring pixels, enabling a more comprehensive texture representation. Additionally, we propose a novel color feature extraction method based on the discrete joint probability distribution of RGB color channels, providing a robust representation of color information. The effectiveness of the proposed method is validated on the Wang (Corel-1k) and Corel-10k datasets, demonstrating superior precision compared to other state-of-the-art techniques. This work contributes to enhancing CBIR performance by combining these novel features into a unified feature vector, improving efficiency and accuracy, especially in large datasets. The code and links to datasets are publicly available at https://github.com/BU-AILab/LABP . Image Retrieval CBIR pattern recognition Local-binary pattern Full Text Additional Declarations No competing interests reported. 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. 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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-5317346","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":370551980,"identity":"f53ce7a2-2a5f-4bb9-be53-031fa1106eea","order_by":0,"name":"Seyyed Ali Hosseini","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/klEQVRIiWNgGAWjYHACNhDBw8DMfIwhAcxJAAlIEKOFLY00LSBdZlBGAn5XybcfYHvw4Y+NjDk7z7cHD/fwyZuzJzB++MFgkY9Li8GZBHbDmW1pPJbNvNsNEp6xGe7secAs2cMgYdmASwtDAps0b8NhHoPDvNskEg6wMW64kcAgDfSLAU6H9T9gk+b58x+ohecZSIs9UAvzb3xaGG4AbeFhOwDSwgbSkrgBJIJPi8GNh22SM9uSgVrYzA2AWpI3nHnYZtljgM9hycckPvyxszc4f/jZwx8HjtluOJ58+MaPijrcDmNgbEDmHYOK4NGADmqIVzoKRsEoGAUjBgAAwFZP6a4Tc94AAAAASUVORK5CYII=","orcid":"","institution":"University of Birjand","correspondingAuthor":true,"prefix":"","firstName":"Seyyed","middleName":"Ali","lastName":"Hosseini","suffix":""},{"id":370551981,"identity":"180f2ffc-1095-4282-98ce-87502ebb3c5b","order_by":1,"name":"AmirHossein Eshghi","email":"","orcid":"","institution":"University of Birjand","correspondingAuthor":false,"prefix":"","firstName":"AmirHossein","middleName":"","lastName":"Eshghi","suffix":""},{"id":370551982,"identity":"2642e1af-55eb-4012-b953-80403b3c3ebd","order_by":2,"name":"Saba Mohammadi","email":"","orcid":"","institution":"University of Birjand","correspondingAuthor":false,"prefix":"","firstName":"Saba","middleName":"","lastName":"Mohammadi","suffix":""},{"id":370551983,"identity":"cf33c42a-d7bd-47dd-bcdf-d63a61fae3f9","order_by":3,"name":"Abdollah Zakeri","email":"","orcid":"","institution":"University of Houston","correspondingAuthor":false,"prefix":"","firstName":"Abdollah","middleName":"","lastName":"Zakeri","suffix":""}],"badges":[],"createdAt":"2024-10-23 08:53:41","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5317346/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5317346/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83736239,"identity":"ce0e35b4-f1b9-4606-bcae-ba25010620d9","added_by":"auto","created_at":"2025-06-01 17:16:37","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":753762,"visible":true,"origin":"","legend":"","description":"","filename":"menuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5317346/v1_covered_422e9f45-6b5c-456b-be13-48495793b558.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Enhanced Content-Based Image Retrieval through Integrated Local Average Binary Patterns and Joint Color Probabilities","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"Image Retrieval, CBIR, pattern recognition, Local-binary pattern","lastPublishedDoi":"10.21203/rs.3.rs-5317346/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5317346/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eArtificial intelligence techniques for image pattern recognition and retrieval are pivotal in various applications, particularly in medical imaging. Despite numerous existing methods, the process remains complex and computationally intensive. This study introduces an innovative approach to content-based image retrieval (CBIR) by integrating Local Average Binary Patterns (LABP) and the joint probability distribution of color channels. LABP extends the traditional Local Binary Pattern (LBP) by considering multiple layers of neighboring pixels, enabling a more comprehensive texture representation. Additionally, we propose a novel color feature extraction method based on the discrete joint probability distribution of RGB color channels, providing a robust representation of color information. The effectiveness of the proposed method is validated on the Wang (Corel-1k) and Corel-10k datasets, demonstrating superior precision compared to other state-of-the-art techniques. This work contributes to enhancing CBIR performance by combining these novel features into a unified feature vector, improving efficiency and accuracy, especially in large datasets. 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