Structural Perceptual Local Feature Pattern

Structural Perceptual Local Feature Pattern | 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Article Structural Perceptual Local Feature Pattern Jie Xu, Yizhi Deng, Jianping Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4952820/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 This paper introduces a novel feature extraction technique, named Structural Perceptual Local Feature Pattern (SPLFP). The SPLFP method integrates Fechner's law into a compact encoding scheme, aiming to mimic the human perception of external stimuli and extract features from images that conform to human visual habits. At the same time, under the framework of the famous Petersen graph, SPLFP extracts spatial structure information from four different directions within the 5×5 neighborhood of each pixel. The effectiveness of SPLFP and its single-scale methods, SPLFPv and SPLFPh, were rigorously evaluated on several face data sets. The results demonstrated that SPLFP and its single-scale methods exhibit remarkable performance, consistency, and stability across all tested datasets. This indicates that SPLFP enables the extraction of highly discriminative features, positioning it as a strong candidate for face classification tasks. Petersen graph feature extractor Fechner's law human perception 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-4952820","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":357430498,"identity":"4c842122-3df3-476d-bfe2-441320a7d838","order_by":0,"name":"Jie Xu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzElEQVRIiWNgGAWjYHCChAMMFQyMDSAmD/FazgC1sJGghYGBsY0ULQY3Eh4e+DivVnbD/QbGB2/bGOTNCWmRnJGQcHDmtuPGG44xMBvObWMw3NlAQAu/RELCYd5txxKBWtikedsYEgwOENDCBtLydw5YC/tvorSAbWFsqAHbwkyUFsmeBwkHe44dMJ55LLFZcs45CcMNhLQYHM9J/vCjpk627/Dhgx/elNnIE7QFGBcJQOIwEIMTgARB9UDADjK1jhiVo2AUjIJRMFIBAAO9Rz4ljDsFAAAAAElFTkSuQmCC","orcid":"","institution":"guangzhou maritime university","correspondingAuthor":true,"prefix":"","firstName":"Jie","middleName":"","lastName":"Xu","suffix":""},{"id":357430499,"identity":"526371f6-8d05-45bd-acfd-d170b7cf2373","order_by":1,"name":"Yizhi Deng","email":"","orcid":"","institution":"Guangdong University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Yizhi","middleName":"","lastName":"Deng","suffix":""},{"id":357430500,"identity":"2dd502eb-e83f-4ef9-b6e0-550dee46ebb2","order_by":2,"name":"Jianping Chen","email":"","orcid":"","institution":"guangzhou maritime university","correspondingAuthor":false,"prefix":"","firstName":"Jianping","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2024-08-21 15:48:54","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4952820/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4952820/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":66284432,"identity":"c3744bd6-ddf4-4fa8-9368-2eec27988966","added_by":"auto","created_at":"2024-10-09 16:32:23","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":852131,"visible":true,"origin":"","legend":"","description":"","filename":"PSPLF.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4952820/v1_covered_c56b6ff1-b28a-4f12-b6bb-3bbf5a13b1f4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Structural Perceptual Local Feature Pattern","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":"Petersen graph, feature extractor, Fechner's law, human perception","lastPublishedDoi":"10.21203/rs.3.rs-4952820/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4952820/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper introduces a novel feature extraction technique, named Structural Perceptual Local Feature Pattern (SPLFP). The SPLFP method integrates Fechner's law into a compact encoding scheme, aiming to mimic the human perception of external stimuli and extract features from images that conform to human visual habits. At the same time, under the framework of the famous Petersen graph, SPLFP extracts spatial structure information from four different directions within the 5\u0026times;5 neighborhood of each pixel. The effectiveness of SPLFP and its single-scale methods, SPLFPv and SPLFPh, were rigorously evaluated on several face data sets. The results demonstrated that SPLFP and its single-scale methods exhibit remarkable performance, consistency, and stability across all tested datasets. This indicates that SPLFP enables the extraction of highly discriminative features, positioning it as a strong candidate for face classification tasks.\u003c/p\u003e","manuscriptTitle":"Structural Perceptual Local Feature Pattern","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-23 17:19:24","doi":"10.21203/rs.3.rs-4952820/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"b1ad4184-1865-48b1-a0fd-45bb238cc6b0","owner":[],"postedDate":"September 23rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-10-09T16:24:11+00:00","versionOfRecord":[],"versionCreatedAt":"2024-09-23 17:19:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4952820","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4952820","identity":"rs-4952820","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}