Content Based Video Retrieval using Deep Learning

Content Based Video Retrieval using Deep Learning | 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 Content Based Video Retrieval using Deep Learning Nitish Palanivelu, Nishita Jagdale, Nishith Dubey, Bharati Jagtap This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4331245/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 Content-based video retrieval (CBVR) is part of a search engine which is widely utilized in everyday life for retrieving relevant images/frames from given large datasets. Generally, the existing methods don't give the relevance and expected images as per the query images and also require more computational time. The CBVR systems depend on the feature representation and similarity measurements. In this project, a CBVR system is developed using deep learning models. The convolutional neural network (CNN) is used to extract features from an input image/frame, and then comparing those features with images in datasets to retrieve similar images/frames. The process involves three main phases namely feature extraction, feature comparison, and retrieval. In feature extraction, the CNN is trained on a dataset of images to learn to extract features that are relevant to the task at hand. In feature comparison, the features extracted from the input image are compared to the features of the images in the database using a similarity measure such as Euclidean distance. Finally, in the retrieval phase, the most similar images/frames from the datasets are returned as the results. The CBVR is carried out using a pre-trained deep learning model, such as VGG16 or ResNet, which is used to train over large image datasets and can extract useful features from new images. In this project, the retrieval of images are carried out and the performance is evaluated based on accuracy, precision, recall. Various CCN models are experimented to perform result analysis. Keywords: Control based video retrieval, Deep learning, Convolutional Neural Network, Information Retrieval, Feature extraction, Inception V3, Feature selection Control based video retrieval Deep learning Convolutional Neural Network Information Retrieval Feature extraction Inception V3 Feature selection 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-4331245","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":295962205,"identity":"df43d7b0-c014-429e-9aca-3b19c4e38217","order_by":0,"name":"Nitish Palanivelu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+UlEQVRIiWNgGAWjYDACZgaGA4wN/+z4mRkbH3wACrCxE9TCDNJyIFmyvfmw4QyQFmZirAFqYdxw5liaNA9UAC8wZ+c/ePDnjjvMDDdyDKRtfm2T52NmYPzwMQe3FstmZobDvGee8THOyDEwzu27bdjGzMAsOXMbbi0Gh4FaGNuYmZklcgySc3tuA9lA7/AS0HLwZxszYxtQy2HLntv2RGk5wNt2mLGH51hiM8OP24nEaDE4zNuWlizB3nyYsbfhdjLQxmb8fjl/8PHHn202dvaHGdt//Phz23Z+e/PBDx/xaEEFjG1gsoFY9SDwhxTFo2AUjIJRMFIAABZBVI3gZxeiAAAAAElFTkSuQmCC","orcid":"","institution":"University of Mumbai","correspondingAuthor":true,"prefix":"","firstName":"Nitish","middleName":"","lastName":"Palanivelu","suffix":""},{"id":295962206,"identity":"5f94bd80-087a-44e2-9539-b9bdb0a7f1a2","order_by":1,"name":"Nishita Jagdale","email":"","orcid":"","institution":"University of Mumbai","correspondingAuthor":false,"prefix":"","firstName":"Nishita","middleName":"","lastName":"Jagdale","suffix":""},{"id":295962207,"identity":"2067b026-d4c7-44e2-bef8-26b86494e206","order_by":2,"name":"Nishith Dubey","email":"","orcid":"","institution":"University of Mumbai","correspondingAuthor":false,"prefix":"","firstName":"Nishith","middleName":"","lastName":"Dubey","suffix":""},{"id":295962208,"identity":"5e2965e9-aa3c-42af-bbf7-fe14800bc641","order_by":3,"name":"Bharati Jagtap","email":"","orcid":"","institution":"University of Mumbai","correspondingAuthor":false,"prefix":"","firstName":"Bharati","middleName":"","lastName":"Jagtap","suffix":""}],"badges":[],"createdAt":"2024-04-26 18:39:47","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4331245/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4331245/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":57079808,"identity":"545322f2-3124-4ca7-b761-7d1a23ba9ec5","added_by":"auto","created_at":"2024-05-24 10:14:40","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":667566,"visible":true,"origin":"","legend":"","description":"","filename":"CBVRUSINGDL1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4331245/v1_covered_bf2c6e3d-609f-4294-9511-73390aa708ea.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Content Based Video Retrieval using Deep Learning","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Control based video retrieval, Deep learning, Convolutional Neural Network, Information Retrieval, Feature extraction, Inception V3, Feature selection","lastPublishedDoi":"10.21203/rs.3.rs-4331245/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4331245/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Content-based video retrieval (CBVR) is part of a search engine which is widely utilized in everyday life for retrieving relevant images/frames from given large datasets. Generally, the existing methods don't give the relevance and expected images as per the query images and also require more computational time. The CBVR systems depend on the feature representation and similarity measurements. In this project, a CBVR system is developed using deep learning models. The convolutional neural network (CNN) is used to extract features from an input image/frame, and then comparing those features with images in datasets to retrieve similar images/frames. The process involves three main phases namely feature extraction, feature comparison, and retrieval. In feature extraction, the CNN is trained on a dataset of images to learn to extract features that are relevant to the task at hand. In feature comparison, the features extracted from the input image are compared to the features of the images in the database using a similarity measure such as Euclidean distance. Finally, in the retrieval phase, the most similar images/frames from the datasets are returned as the results. The CBVR is carried out using a pre-trained deep learning model, such as VGG16 or ResNet, which is used to train over large image datasets and can extract useful features from new images. In this project, the retrieval of images are carried out and the performance is evaluated based on accuracy, precision, recall. 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