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Exploring the Representativity of Art Paintings

文献类型：期刊论文

作者Deng, Yingying1,2; Tang, Fan1,2; Dong, Weiming1,2; Ma, Chongyang3; Huang, Feiyue4; Deussen, Oliver5; Xu, Changsheng1,2
刊名IEEE TRANSACTIONS ON MULTIMEDIA
出版日期2021
卷号23页码:2794-2805
关键词Painting Art Image color analysis Feature extraction Task analysis Engineering profession Electronic mail Representativity style enhancement feature representation artwork evaluation
ISSN号1520-9210
DOI10.1109/TMM.2020.3016887
通讯作者Dong, Weiming(weiming.dong@ia.ac.cn)
英文摘要Art painting evaluation is sophisticated for a novice with no or limited knowledge on art criticism, and history. In this study, we propose the concept of representativity to evaluate paintings instead of using professional concepts, such as genre, media, and style, which may be confusing to non-professionals. We define the concept of representativity to evaluate quantitatively the extent to which a painting can represent the characteristics of an artists creations. We begin by proposing a novel deep representation of art paintings, which is enhanced by style information through a weighted pooling feature fusion module. In contrast to existing feature extraction approaches, the proposed framework embeds painting styles, and authorship information, and learns specific artwork characteristics in a single framework. Subsequently, we propose a graph-based learning method for representativity learning, which considers intra-category, and extra-category information. In view of the significance of historical factors in the art domain, we introduce the creation time of a painting into the learning process. User studies demonstrate our approach helps the public effectively access the creation characteristics of artists through sorting paintings by representativity from highest to lowest.
WOS关键词STYLE ; AESTHETICS ; DISCOVERY
资助项目National Natural Science Foundation of China[61832016] ; National Natural Science Foundation of China[61672520] ; National Natural Science Foundation of China[61702488] ; CASIA-Tencent Youtu joint research project
WOS研究方向Computer Science ; Telecommunications
语种英语
WOS记录号WOS:000688215600020
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构National Natural Science Foundation of China ; CASIA-Tencent Youtu joint research project
源URL[http://ir.ia.ac.cn/handle/173211/45878]  
专题自动化研究所_模式识别国家重点实验室_多媒体计算与图形学团队
通讯作者Dong, Weiming
作者单位1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
2.Chinese Acad Sci, Inst Automat, NLPR, Beijing 100190, Peoples R China
3.Kuaishou Technol, Beijing 100085, Peoples R China
4.Tencent, YouTu Lab, Shanghai 200233, Peoples R China
5.Univ Konstanz, D-78464 Constance, Germany
推荐引用方式
GB/T 7714		 Deng, Yingying,Tang, Fan,Dong, Weiming,et al. Exploring the Representativity of Art Paintings[J]. IEEE TRANSACTIONS ON MULTIMEDIA,2021,23:2794-2805.
APA Deng, Yingying.,Tang, Fan.,Dong, Weiming.,Ma, Chongyang.,Huang, Feiyue.,...&Xu, Changsheng.(2021).Exploring the Representativity of Art Paintings.IEEE TRANSACTIONS ON MULTIMEDIA,23,2794-2805.
MLA Deng, Yingying,et al."Exploring the Representativity of Art Paintings".IEEE TRANSACTIONS ON MULTIMEDIA 23(2021):2794-2805.

入库方式： OAI收割

来源：自动化研究所

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