Optimal Deep  Barcodes for Fast Image Retrieval

Optimal Deep Barcodes for Fast Image Retrieval | 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 Article Optimal Deep Barcodes for Fast Image Retrieval Rasa Khosrowshahli, Farnaz Kheiri, Azam Asilian Bidgoli, Hamid Reza Tizhoosh, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4818178/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 Binary encoding of features has proven to be a versatile tool for optimizing data processing and memory efficiency in various machine learning applications. This includes deep barcoding of images for retrieval of similar cases among millions of indexed images. Despite the recent advancement in barcode generation methods, converting hyperdimensional feature vectors (e.g., deep features) to compact and discriminative binary barcodes is still an urgent necessity and widely an unresolved problem. MinMax barcoding of features is one of the efficient binarization methods, where the accuracy of the generated barcodes is inherently dependent on the feature order due to its computation process. In this paper, we propose a combinatorial evolutionary framework for optimizing barcodes. The primary goal is to determine an optimal permutation of extracted deep features, leading to more accurate barcodes. The performance of the proposed model has been assessed in downstream image retrieval tasks using a variety of datasets. This evaluation includes medical images from The Cancer Genome Atlas (TCGA), a comprehensive publicly available dataset, as well as images from a COVID-19 dataset. In addition, the model's performance was tested on diverse non-medical image collections, such as CIFAR-10 and Fashion-MNIST. Our findings demonstrate that optimizing barcodes significantly enhances retrieval accuracy across a wide range of applications, highlighting the applicability and potential of barcodes in various domains. Biological sciences/Computational biology and bioinformatics/Machine learning Physical sciences/Mathematics and computing/Computational science Physical sciences/Mathematics and computing/Computer science 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. 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-4818178","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":348164000,"identity":"30f52114-eeed-4aea-9a41-cdb1a255def1","order_by":0,"name":"Rasa Khosrowshahli","email":"","orcid":"","institution":"Brock University","correspondingAuthor":false,"prefix":"","firstName":"Rasa","middleName":"","lastName":"Khosrowshahli","suffix":""},{"id":348164001,"identity":"730bc609-94bd-422b-bc45-798812a3ff68","order_by":1,"name":"Farnaz Kheiri","email":"","orcid":"","institution":"University of Ontario Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Farnaz","middleName":"","lastName":"Kheiri","suffix":""},{"id":348164002,"identity":"3cdab9a5-aee6-4494-a9f0-82fbaf26f453","order_by":2,"name":"Azam Asilian Bidgoli","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8UlEQVRIiWNgGAWjYJCCAw8MGBgMmBmAZIGEHAM7MVoS4FoMJIwZmImxJgGIDaAosYGQFv724w8PJBTY5ZmzM29+8cHAIn3DYR4Dhh81uLVInMkxADosudiyma3McoaBRC5IC2PPMXweyQH5hTkRqNLMmAeoZRtQCzMDG24d8uefPwBqqYdrSTcDa/mHW4vBjQSQww6DtBg/BmpJAGthbMOtxfDGG5CW44k7gX5hBPrFcP9htoKDvX24tcidT3/84cOf6sTt/Ic3f/hQUScv2d688cGPb3i8jwTYJGCsA8RpYGBg/kCsylEwCkbBKBhZAAB/FlJrIMG8fwAAAABJRU5ErkJggg==","orcid":"","institution":"Wilfrid Laurier University","correspondingAuthor":true,"prefix":"","firstName":"Azam","middleName":"Asilian","lastName":"Bidgoli","suffix":""},{"id":348164003,"identity":"709c48f3-c0d5-47d3-9c94-ded87d591560","order_by":3,"name":"Hamid Reza Tizhoosh","email":"","orcid":"","institution":"Mayo Clinic","correspondingAuthor":false,"prefix":"","firstName":"Hamid","middleName":"Reza","lastName":"Tizhoosh","suffix":""},{"id":348164004,"identity":"59515a3a-35c6-457c-b2ba-62f2dac8c63f","order_by":4,"name":"Masoud Makrehchi","email":"","orcid":"","institution":"University of Ontario Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Masoud","middleName":"","lastName":"Makrehchi","suffix":""},{"id":348164005,"identity":"a1757406-8ebe-490f-b58b-62c6950c6371","order_by":5,"name":"Shahryar Rahnamayan","email":"","orcid":"","institution":"Brock University","correspondingAuthor":false,"prefix":"","firstName":"Shahryar","middleName":"","lastName":"Rahnamayan","suffix":""}],"badges":[],"createdAt":"2024-07-28 22:23:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4818178/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4818178/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":65189897,"identity":"50a25084-5421-45f9-82a9-3397922cded1","added_by":"auto","created_at":"2024-09-24 14:24:35","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1227317,"visible":true,"origin":"","legend":"","description":"","filename":"ktsdsnzbyqvngpcrmqnnmpdjwrhynhwv.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4818178/v1_covered_0846a578-6947-4142-ad98-25444f9096df.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Optimal Deep Barcodes for Fast Image Retrieval","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":"","lastPublishedDoi":"10.21203/rs.3.rs-4818178/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4818178/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Binary encoding of features has proven to be a versatile tool for optimizing data processing and memory efficiency in various machine learning applications. 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