Pottery Evolution Pattern Discovery based on Deep Learning: Case Study of Miaozigou Culture in China

Pottery Evolution Pattern Discovery based on Deep Learning: Case Study of Miaozigou Culture in China | 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 Pottery Evolution Pattern Discovery based on Deep Learning: Case Study of Miaozigou Culture in China Honglin Pang, Xiujin Qi, Chengjun Xiao, Ziying Xu, Guangchen Ding, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4673638/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 10 Oct, 2024 Read the published version in npj Heritage Science → Version 1 posted 4 You are reading this latest preprint version Abstract Potteries, one of the tools widely used by early humans, encapsulates rich historical information. Deep neural networks have been applied to analyzing pottery digital images, bypassing the need for intricate handcrafted features. However, existing models focus solely on pottery shape comparison, neglecting the analysis of their evolution across different historical periods. In this work, we propose a method based on deep learning to assist experts in identifying the evolutionary patterns of a given pottery type within their specified chronological divisions. First we train a convolutional neural network for pottery classification, extracting low and high level features that represent different ages of pottery samples. Next, we employ clustering algorithms to identify representative potteries for each historical period based on high level features. To facilitate intuitive comparisons across different ages, we use shallow features and compute cosine similarities between potteries, visualizing shape and decoration differences. This approach enhances understanding of pottery evolution patterns directly through visual analysis. The effectiveness and efficiency of our proposed method are evaluated by validating it on three distinct era division cases using data from the Dabagou and Miaozigou archaeological sites, which represent the Miaozigou culture and exhibit clear evolutionary patterns. Our method identifies representative artifacts for each era and uncovers their evolutionary patterns effectively and efficiently, achieving conclusions comparable to those of experts while significantly reducing time compared to traditional manual methods. Potteries evolution Deep Learning Clustering Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 10 Oct, 2024 Read the published version in npj Heritage Science → Version 1 posted Editorial decision: Revision requested 05 Jul, 2024 Editor assigned by journal 05 Jul, 2024 Submission checks completed at journal 05 Jul, 2024 First submitted to journal 02 Jul, 2024 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-4673638","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":323218994,"identity":"f6ae1c62-a717-4356-b100-a12d1d2071a5","order_by":0,"name":"Honglin Pang","email":"","orcid":"","institution":"Jilin University","correspondingAuthor":false,"prefix":"","firstName":"Honglin","middleName":"","lastName":"Pang","suffix":""},{"id":323218996,"identity":"0bbafa24-fbc3-4e7f-91f7-1d5ba27c25d0","order_by":1,"name":"Xiujin Qi","email":"","orcid":"","institution":"Jilin 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