A joint similarity matrix learning of multi-view data for RGB-T saliency detection

A joint similarity matrix learning of multi-view data for RGB-T saliency detection | 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 A joint similarity matrix learning of multi-view data for RGB-T saliency detection Fan Wang, Yanhong She, Weiping Ding, Yuhua Qian, Guohua Peng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4421995/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 Graph-based saliency detection has aroused considerable attention due to its ability to extract object of interest from the natural scenes. However, the most existing methods usually consisted the graph constrcution and initial back-gorund/foreground seeds selection, which may simplify the relationship among multi-view data and results in an inaccurate in complicated scenes. To our knowledge , the success of the graph-based saliency detection methods mainly rely on the qulaity of graph. In this paper, we target to achieve a joint similarity matrix learning (AJSML) from multi-view data based on graph diffusion process, which is committed to facilitating the RGB-T saliency detection task. Our assumption is that salient object in the complicaed scene always tend to be similarity appearance and compactness distribution in spatial domain. Specifically, we first design a generalized framework to simultaneously learn the high quality graph and similarity matrix for multi-view data. Thus, the similarity relationship and correlation information of multi-view data can be effectively diffused on the high quality graph, which is conducive to generating a faithful similarity matrix. Further, we present a post-processing technology called an adaptive weighted semi-supervised 1 learning (AWSL), integrating saliency information and cross-modality graphs, is developed to promote the accuracy degree of saliency results. Finally, extensive experimental results on well-known benchmark RGB-T, RGB, and RGB-D datasets demonstrate the superiority of the proposed method, in comparison to several state-of-the-art methods. RGB-T saliency detection Similarity matrix Graph diffusion process Multi-view data Semi-supervised learning 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-4421995","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":305962211,"identity":"3183d92a-64ea-4c7a-a842-33b91180cda7","order_by":0,"name":"Fan Wang","email":"","orcid":"","institution":"Xi’an Shiyou University","correspondingAuthor":false,"prefix":"","firstName":"Fan","middleName":"","lastName":"Wang","suffix":""},{"id":305962212,"identity":"22516beb-60c4-4013-88af-9f9f842d561d","order_by":1,"name":"Yanhong She","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8klEQVRIiWNgGAWjYHACNjBpwMB8ACqQQLQWNphS4rXwGBCnxeD42WMPPu6otTdn7/km+bPtMAM/e44Bw88deLScyUs3nHnmeOLOnrObjXmBWiR73hgw9p7Bo+VAjpk0b9uxBIMbuRsfMwK1GNzIMWBmbMOj5fwbsBZ7g/tvHhwEOcyeoJYbYFtqGDfc4GF8AHKYgQQBLZI33phJzmw7kLjhTJqxMc+5dB6JM88KDvbi0cJ3PsdM4mNbnb3B8cPPJH+UWcvxtydvfPATjxaFA2DqMITHyMbAA6IP4NbAwCDfAKbqoNw/+NSOglEwCkbBSAUAzUJWQ03yDx4AAAAASUVORK5CYII=","orcid":"","institution":"Xi’an Shiyou University","correspondingAuthor":true,"prefix":"","firstName":"Yanhong","middleName":"","lastName":"She","suffix":""},{"id":305962213,"identity":"0752e441-8175-4244-a1b7-dfcc0c0e2094","order_by":2,"name":"Weiping Ding","email":"","orcid":"","institution":"Nantong University","correspondingAuthor":false,"prefix":"","firstName":"Weiping","middleName":"","lastName":"Ding","suffix":""},{"id":305962214,"identity":"972e3021-419d-47f4-abec-233b5e9b3576","order_by":3,"name":"Yuhua Qian","email":"","orcid":"","institution":"Shanxi University","correspondingAuthor":false,"prefix":"","firstName":"Yuhua","middleName":"","lastName":"Qian","suffix":""},{"id":305962215,"identity":"7fbe1536-a322-424c-93b1-bc5690312ae2","order_by":4,"name":"Guohua Peng","email":"","orcid":"","institution":"Northwestern Polytechnical University","correspondingAuthor":false,"prefix":"","firstName":"Guohua","middleName":"","lastName":"Peng","suffix":""}],"badges":[],"createdAt":"2024-05-15 02:23:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4421995/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4421995/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":59566006,"identity":"4de58a7b-c515-4aa0-9fe8-c19f2fe1e332","added_by":"auto","created_at":"2024-07-03 09:11:51","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7802880,"visible":true,"origin":"","legend":"","description":"","filename":"snarticle.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4421995/v1_covered_09b36f76-d2d0-471b-b06f-4b21e6311058.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A joint similarity matrix learning of multi-view data for RGB-T saliency detection","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":"RGB-T saliency detection, Similarity matrix, Graph diffusion process, Multi-view data, Semi-supervised learning","lastPublishedDoi":"10.21203/rs.3.rs-4421995/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4421995/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Graph-based saliency detection has aroused considerable attention due to its ability to extract object of interest from the natural scenes. 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