Deep Learning based Swapping Generative Framework for Rapid Cloth Retrieval

Deep Learning based Swapping Generative Framework for Rapid Cloth 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 Research Article Deep Learning based Swapping Generative Framework for Rapid Cloth Retrieval Ajitha Gladis K. P, Srinivasan R, Sangeethapriya S, Jayapriya P This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3887154/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 Modern life is fast-paced, and every people is very busy with their daily routines. The online shopping option in E-commerce is a great time-saver in such a scenario. Moreover, it is crucial to extract accurate product features in order to search, recommend, classify, and retrieve images based on fashion queries.To overcome the forementioned challenges, a novel cloth swapping GAN based fashion retrieval has been introduced for rapid retrieval of relevant fashion based on the user query. Initially, to reduce the computational time, GrabCut is used to remove the background of the cloth images.The Cloth encoding decoding-based parsing Network is introduced to segment the bottom and top of the cloth. Then, the separated cloth region is fed into the GAN based on the user preference. The threshold neural network (TNN) is integrated with gates for efficient feature extraction in a small fraction of time. The feature extraction process is performed based on the feedback of the user. The extracted features such as dress length (long, medium, short), dress sleeve (sleeveless, full sleeve, half sleeve), and dress pattern (designs, dots, straights) are used to retrieve the relevant clothes for the users based on the query from the online shops. The proposed model achieves atotal accuracy of 99.29%. The proposed cloth retrieval system enhances the total accuracy by 14.24%, 8.75%, and 23.55% better than Alexnet, cGAN, and CNN, respectively. Cloth retrieval GrabCut Deep learning GAN Threshold neural network Cloth Parsing Encoder Decoder Network 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-3887154","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":268842132,"identity":"cc4d7a09-f704-4b85-92ad-8fe3c8962d93","order_by":0,"name":"Ajitha Gladis K. 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The online shopping option in E-commerce is a great time-saver in such a scenario. Moreover, it is crucial to extract accurate product features in order to search, recommend, classify, and retrieve images based on fashion queries.To overcome the forementioned challenges, a novel cloth swapping GAN based fashion retrieval has been introduced for rapid retrieval of relevant fashion based on the user query. Initially, to reduce the computational time, GrabCut is used to remove the background of the cloth images.The Cloth encoding decoding-based parsing Network is introduced to segment the bottom and top of the cloth. Then, the separated cloth region is fed into the GAN based on the user preference. The threshold neural network (TNN) is integrated with gates for efficient feature extraction in a small fraction of time. The feature extraction process is performed based on the feedback of the user. The extracted features such as dress length (long, medium, short), dress sleeve (sleeveless, full sleeve, half sleeve), and dress pattern (designs, dots, straights) are used to retrieve the relevant clothes for the users based on the query from the online shops. The proposed model achieves atotal accuracy of 99.29%. 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