从空间角度探讨中国出土壁画的视觉可分辨性和主题自相关性

IF 1.8 4区 管理学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Journal of Information Science Pub Date : 2023-10-24 DOI:10.1177/01655515231202761
Shouqiang Sun, Ziming Zeng, Qingqing Li
{"title":"从空间角度探讨中国出土壁画的视觉可分辨性和主题自相关性","authors":"Shouqiang Sun, Ziming Zeng, Qingqing Li","doi":"10.1177/01655515231202761","DOIUrl":null,"url":null,"abstract":"Murals unearthed in China have outstanding regional characteristics and one of the largest period spans in scale and variety. To explore the visual distinguishability and topic autocorrelation of murals unearthed in China from the spatial perspective, multiple classification models are employed to classify murals unearthed in China through visual features. Then, the k-means is employed to mine topics, and they are analysed through topic intensities (TIs), Moran’s Index (MI) and spatial topic concentration degrees (STCDs). In addition, the characteristics of topic distribution and evolution are summarised and revealed in the spatial dimension. From a spatial perspective, it verifies the distinguishability of visual features of murals through ViT_BOW_GNB, and the precision of this model is 98.17%. Thirteen topics are clustered through k-means, and the distribution of mural topics is spatial autocorrelation according to MI. Besides, the topic evolves from the political centre to the surrounding area, and the topics with high intensities are highly concentrated in spatial. This study reveals the spatial characteristics of the mural at the level of visual features and semantics, which facilitates the digital management, conservation and knowledge discovery of cultural heritage resources.","PeriodicalId":54796,"journal":{"name":"Journal of Information Science","volume":"31 5","pages":"0"},"PeriodicalIF":1.8000,"publicationDate":"2023-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Exploring the visual distinguishability and topic autocorrelation of murals unearthed in China from the spatial perspective\",\"authors\":\"Shouqiang Sun, Ziming Zeng, Qingqing Li\",\"doi\":\"10.1177/01655515231202761\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Murals unearthed in China have outstanding regional characteristics and one of the largest period spans in scale and variety. To explore the visual distinguishability and topic autocorrelation of murals unearthed in China from the spatial perspective, multiple classification models are employed to classify murals unearthed in China through visual features. Then, the k-means is employed to mine topics, and they are analysed through topic intensities (TIs), Moran’s Index (MI) and spatial topic concentration degrees (STCDs). In addition, the characteristics of topic distribution and evolution are summarised and revealed in the spatial dimension. From a spatial perspective, it verifies the distinguishability of visual features of murals through ViT_BOW_GNB, and the precision of this model is 98.17%. Thirteen topics are clustered through k-means, and the distribution of mural topics is spatial autocorrelation according to MI. Besides, the topic evolves from the political centre to the surrounding area, and the topics with high intensities are highly concentrated in spatial. This study reveals the spatial characteristics of the mural at the level of visual features and semantics, which facilitates the digital management, conservation and knowledge discovery of cultural heritage resources.\",\"PeriodicalId\":54796,\"journal\":{\"name\":\"Journal of Information Science\",\"volume\":\"31 5\",\"pages\":\"0\"},\"PeriodicalIF\":1.8000,\"publicationDate\":\"2023-10-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Information Science\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1177/01655515231202761\",\"RegionNum\":4,\"RegionCategory\":\"管理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Information Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1177/01655515231202761","RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0

摘要

中国出土壁画地域特征突出,在规模和种类上都是世界上年代跨度最大的壁画之一。从空间角度探讨中国出土壁画的视觉可分辨性和主题自相关性,采用多种分类模型,通过视觉特征对中国出土壁画进行分类。然后,利用k-means对话题进行挖掘,并通过话题强度(TIs)、莫兰指数(MI)和空间话题集中度(stcd)对话题进行分析。此外,从空间维度上总结和揭示了话题分布和演变的特征。从空间角度,通过ViT_BOW_GNB验证壁画视觉特征的可分辨性,该模型的准确率为98.17%。通过k-means聚类了13个话题,根据MI,壁画话题的分布是空间自相关的,并且话题从政治中心向周边地区演变,高强度的话题在空间上高度集中。本研究在视觉特征和语义层面揭示了壁画的空间特征,为文化遗产资源的数字化管理、保护和知识发现提供了便利。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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
Exploring the visual distinguishability and topic autocorrelation of murals unearthed in China from the spatial perspective
Murals unearthed in China have outstanding regional characteristics and one of the largest period spans in scale and variety. To explore the visual distinguishability and topic autocorrelation of murals unearthed in China from the spatial perspective, multiple classification models are employed to classify murals unearthed in China through visual features. Then, the k-means is employed to mine topics, and they are analysed through topic intensities (TIs), Moran’s Index (MI) and spatial topic concentration degrees (STCDs). In addition, the characteristics of topic distribution and evolution are summarised and revealed in the spatial dimension. From a spatial perspective, it verifies the distinguishability of visual features of murals through ViT_BOW_GNB, and the precision of this model is 98.17%. Thirteen topics are clustered through k-means, and the distribution of mural topics is spatial autocorrelation according to MI. Besides, the topic evolves from the political centre to the surrounding area, and the topics with high intensities are highly concentrated in spatial. This study reveals the spatial characteristics of the mural at the level of visual features and semantics, which facilitates the digital management, conservation and knowledge discovery of cultural heritage resources.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Journal of Information Science
Journal of Information Science 工程技术-计算机:信息系统
CiteScore
6.80
自引率
8.30%
发文量
121
审稿时长
4 months
期刊介绍: The Journal of Information Science is a peer-reviewed international journal of high repute covering topics of interest to all those researching and working in the sciences of information and knowledge management. The Editors welcome material on any aspect of information science theory, policy, application or practice that will advance thinking in the field.
期刊最新文献
Government chatbot: Empowering smart conversations with enhanced contextual understanding and reasoning Knowing within multispecies families: An information experience study How are global university rankings adjusted for erroneous science, fraud and misconduct? Posterior reduction or adjustment in rankings in response to retractions and invalidation of scientific findings Predicting the technological impact of papers: Exploring optimal models and most important features Cross-domain corpus selection for cold-start context
×
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