From Pixels to Titles: Video Game Identification by Screenshots using Convolutional Neural Networks

From Pixels to Titles: Video Game Identification by Screenshots using Convolutional Neural Networks | 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 From Pixels to Titles: Video Game Identification by Screenshots using Convolutional Neural Networks Fabricio Breve This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4378847/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 investigates video game identification through single screenshots, utilizing five convolutional neural network (CNN) architectures (MobileNet, DenseNet, EfficientNetB0, EfficientNetB2, and EfficientNetB3) across 22 home console systems, spanning from Atari 2600 to PlayStation 5, totalling 8,796 games and 170,881 screenshots. Confirming the hypothesis, CNNs autonomously extract image features, enabling the identification of game titles from screenshots without additional features. Using ImageNet pre-trained weights as initial weights, EfficientNetB3 achieves the highest average accuracy (74.51%), while DenseNet169 excels in 14 of the 22 systems. Employing alternative initial weights trained in an arcade screenshots dataset boosts accuracy for EfficientNetB2 and EfficientNetB3, with the latter reaching a peak accuracy of 76.36% and demonstrating reduced convergence epochs from 23.7 to 20.5 on average. Overall, the combination of optimal architecture and weights attains 77.67% accuracy, primarily led by EfficientNetB3 in 19 systems. These findings underscore the efficacy of CNNs in video game identification through screenshots. video game identification convolutional neural networks transfer learning single screenshot analysis automated game recognition 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-4378847","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":302733631,"identity":"91f3a732-827d-49d3-8b8c-b3ba8a4ede4e","order_by":0,"name":"Fabricio Breve","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6klEQVRIiWNgGAWjYBAC9gYeBoYEBgkGEAICG4gwYwNuLTwHULWkEakFDCBaDhOhhf3ssQ8PaizkGKR7Hz7mqTifuJ29/dkDxh33cGvhyUuekXBMwphB5rixMc+Z24k7e86YGzCeKcapxZ4hx5ghsUECiNLYJGe23U7ccCOHTYKxLQG3Lfxv4FrYf878dy5xw/3nz/BrkUCyheFjwwGgLQxmBLS8S2YA+YVN5hizxIdjycYbzuSYGySeweew3MOMP2rq5Pil2xg/JNTYyW44fvzZg487cGuBAzYUNhEacGsfBaNgFIyCUcAAAO66UQWSmo94AAAAAElFTkSuQmCC","orcid":"","institution":"São Paulo State University","correspondingAuthor":true,"prefix":"","firstName":"Fabricio","middleName":"","lastName":"Breve","suffix":""}],"badges":[],"createdAt":"2024-05-06 19:39:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4378847/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4378847/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":57310309,"identity":"07e97b98-1f9c-49c2-8ab4-fe9f289d36df","added_by":"auto","created_at":"2024-05-29 02:52:18","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1961909,"visible":true,"origin":"","legend":"","description":"","filename":"MVAvideogame.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4378847/v1_covered_10ff9edc-a104-4386-ac07-eaf3b23e9662.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"From Pixels to Titles: Video Game Identification by Screenshots using Convolutional Neural Networks","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":"video game identification, convolutional neural networks, transfer learning, single screenshot analysis, automated game recognition","lastPublishedDoi":"10.21203/rs.3.rs-4378847/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4378847/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"This paper investigates video game identification through single screenshots, utilizing five convolutional neural network (CNN) architectures (MobileNet, DenseNet, EfficientNetB0, EfficientNetB2, and EfficientNetB3) across 22 home console systems, spanning from Atari 2600 to PlayStation 5, totalling 8,796 games and 170,881 screenshots. 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