CASIA OpenIR  > 多媒体计算与图形学团队
Selective clustering for representative paintings selection
Deng, Yingying1,2,4; Tang, Fan1,2,4; Dong, Weiming1; Wu, Fuzhang3; Deussen, Oliver; Xu, Changsheng1
Source PublicationMULTIMEDIA TOOLS AND APPLICATIONS
ISSN1380-7501
2019-07-01
Volume78Issue:14Pages:19305-19323
Corresponding AuthorDong, Weiming(weiming.dong@ia.ac.cn)
AbstractSelective classification (or rejection based classification) has been proved useful in many applications. In this paper we describe a selective clustering framework with reject option to carry out large-scale digital arts analysis. With the help of deep learning techniques, we extract content-style features from a pre-trained convolutional network for the paintings. By proposing a rejection mechanism under Bayesian framework, we focus on selecting style-oriented representative paintings of an artist, which is an interesting and challenging cultural heritage application. Two kinds of samples are rejected during the rejection based robust continuous clustering process. Representative paintings are selected during the selective clustering phase. Visual qualitative analysis on small painting set and large scale quantitative experiments on a subset of Wikiart show that the proposed rejection based selective clustering approach outperforms the standard clustering methods.
KeywordDigital arts analysis Pattern mining Rejection mechanism Deep feature representation
DOI10.1007/s11042-019-7271-7
WOS KeywordREJECT OPTION ; CLASSIFICATION ; STYLE
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[61832016] ; National Natural Science Foundation of China[61672520] ; National Natural Science Foundation of China[61702488] ; National Laboratory of Pattern Recognition
Funding OrganizationNational Natural Science Foundation of China ; National Laboratory of Pattern Recognition
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Information Systems ; Computer Science, Software Engineering ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS IDWOS:000475703800017
PublisherSPRINGER
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/23909
Collection多媒体计算与图形学团队
Corresponding AuthorDong, Weiming
Affiliation1.Chinese Acad Sci, Inst Automat, NLPR LIAMA, Beijing, Peoples R China
2.Univ Chinese Acad Sci, Beijing, Peoples R China
3.Chinese Acad Sci, Inst Software, Beijing, Peoples R China
4.Univ Konstanz, Constance, Germany
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Deng, Yingying,Tang, Fan,Dong, Weiming,et al. Selective clustering for representative paintings selection[J]. MULTIMEDIA TOOLS AND APPLICATIONS,2019,78(14):19305-19323.
APA Deng, Yingying,Tang, Fan,Dong, Weiming,Wu, Fuzhang,Deussen, Oliver,&Xu, Changsheng.(2019).Selective clustering for representative paintings selection.MULTIMEDIA TOOLS AND APPLICATIONS,78(14),19305-19323.
MLA Deng, Yingying,et al."Selective clustering for representative paintings selection".MULTIMEDIA TOOLS AND APPLICATIONS 78.14(2019):19305-19323.
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