Enhancing Image Classification via PPSEAUG: A Plug-and-Play Segmentation-Guided Augmentation Framework

Enhancing Image Classification via PPSEAUG: A Plug-and-Play Segmentation-Guided Augmentation Framework | 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 Enhancing Image Classification via PPSEAUG: A Plug-and-Play Segmentation-Guided Augmentation Framework Jinhui Lin, Siqi Yang, Fengjie Chen, Yan Liu, Xiaobin Zhu, Xu-Cheng Yin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7596556/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Image classification performance is heavily reliant on the quality and diversity of training data. Traditional methods often fall short due to the high cost and labour intensity of obtaining high-quality segmentation annotations. This paper introduces PPSEAUG, a plug-and-play segmentation-guided augmentation framework that leverages lightweight segmentation models to enhance classification accuracy. By combining uncertainty estimation and information gain optimization, PPSEAUG effectively mitigates the impact of low-quality samples. Evaluations on Mini-ImageNet and Caltech-101 datasets demonstrate that PPSEAUG consistently outperforms traditional augmentation methods, improving Top-1 accuracy by up to 10.25% on ResNet-50 and by 5.25% on RepViT on the Caltech-101 dataset. These results confirm the practicality and effectiveness of PPSEAUG in enhancing image classification models,Our code is available at https://github.com/SEGAUG/PPSEAUG . Image classification Uncertainty Information gain optimization Segmentation Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 19 Apr, 2026 Reviews received at journal 23 Dec, 2025 Reviewers agreed at journal 18 Dec, 2025 Reviewers agreed at journal 12 Nov, 2025 Reviewers agreed at journal 09 Nov, 2025 Reviewers invited by journal 09 Nov, 2025 Editor assigned by journal 12 Sep, 2025 Submission checks completed at journal 12 Sep, 2025 First submitted to journal 12 Sep, 2025 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. 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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-7596556","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":543932995,"identity":"e7d73a82-7a5b-4aa9-a0f9-9d93fbd7ccb4","order_by":0,"name":"Jinhui 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