CNN-based classification of Persian miniature paintings from five renowned schools

Mojtaba Shahi , Roozbeh Rajabi , Farnaz Masoumzadeh
{"title":"CNN-based classification of Persian miniature paintings from five renowned schools","authors":"Mojtaba Shahi ,&nbsp;Roozbeh Rajabi ,&nbsp;Farnaz Masoumzadeh","doi":"10.1016/j.daach.2024.e00397","DOIUrl":null,"url":null,"abstract":"<div><div>This article addresses the gap in computational painting analysis focused on Persian miniature painting, a rich cultural and artistic heritage. It introduces a novel approach using Convolutional Neural Networks (CNN) to classify Persian miniatures from five schools: Herat, Tabriz-e Avval, Shiraz-e Avval, Tabriz-e Dovvom, and Qajar. The method achieves an average accuracy of over 91%. A meticulously curated dataset captures the distinct features of each school, with a patch-based CNN approach classifying image segments independently before merging results for enhanced accuracy. This research contributes significantly to digital art analysis, providing detailed insights into the dataset, CNN architecture, training, and validation processes. It highlights the potential for future advancements in automated art analysis, bridging machine learning, art history, and digital humanities, thereby aiding the preservation and understanding of Persian cultural heritage.</div></div>","PeriodicalId":38225,"journal":{"name":"Digital Applications in Archaeology and Cultural Heritage","volume":"36 ","pages":"Article e00397"},"PeriodicalIF":0.0000,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Digital Applications in Archaeology and Cultural Heritage","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2212054824000833","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Social Sciences","Score":null,"Total":0}
引用次数: 0

Abstract

This article addresses the gap in computational painting analysis focused on Persian miniature painting, a rich cultural and artistic heritage. It introduces a novel approach using Convolutional Neural Networks (CNN) to classify Persian miniatures from five schools: Herat, Tabriz-e Avval, Shiraz-e Avval, Tabriz-e Dovvom, and Qajar. The method achieves an average accuracy of over 91%. A meticulously curated dataset captures the distinct features of each school, with a patch-based CNN approach classifying image segments independently before merging results for enhanced accuracy. This research contributes significantly to digital art analysis, providing detailed insights into the dataset, CNN architecture, training, and validation processes. It highlights the potential for future advancements in automated art analysis, bridging machine learning, art history, and digital humanities, thereby aiding the preservation and understanding of Persian cultural heritage.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
本文针对波斯微型绘画这一丰富的文化和艺术遗产,探讨了计算绘画分析方面的空白。文章介绍了一种使用卷积神经网络(CNN)对五个流派的波斯微缩画进行分类的新方法:赫拉特、大不里士-埃阿瓦尔、设拉子-埃阿瓦尔、大不里士-埃多夫沃姆和卡扎尔。该方法的平均准确率超过 91%。精心策划的数据集捕捉了每所学校的独特特征,采用基于补丁的 CNN 方法对图像片段进行独立分类,然后合并结果以提高准确性。这项研究为数字艺术分析做出了重大贡献,提供了有关数据集、CNN 架构、训练和验证过程的详细见解。它凸显了未来自动艺术分析的发展潜力,是机器学习、艺术史和数字人文的桥梁,从而有助于保护和了解波斯文化遗产。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
CiteScore
5.40
自引率
0.00%
发文量
33
期刊最新文献
GIS, sight and sound. Exploring the rock art landscapes of the Santa Teresa Canyon (Baja California Sur, Mexico) as a case study Enhancing multicultural visitor engagement through digital art installations: A case study at DPirang, Tongyeong, South Korea Breaking the barriers: Extended reality and innovative technologies for enhanced accessibility of the Ceramics Museum of Cutrofiano A study on levels of detail for presenting 3D models of historical costumes online CNN-based classification of Persian miniature paintings from five renowned schools
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1