UM
Detecting Chinese Calligraphy Style Consistency by Deep Learning and One-Class SVM
Zhang Jiulong; Guo Luming; Yang Su; Sun Xudong; Li Xiaoshan; IEEE
2017
Conference Name2017 2ND INTERNATIONAL CONFERENCE ON IMAGE, VISION AND COMPUTING (ICIVC 2017)
Pages83-86
Publication Place345 E 47TH ST, NEW YORK, NY 10017 USA
PublisherIEEE
AbstractWhen beginners practice Chinese calligraphy, they often copy from ancient calligraphic works and try to imitate the style as closely as possible. However there are inevitably some characters whose styles are not correctly followed. Thus we are motivated to detect the style consistency of all written characters in one practice. With the styles extracted by using stacked autoencoders of deep neural network model, we discriminate correctly styled and alien styled characters using a trained one-class support vector machine. Thus we can pick out those outliers. The proposed algorithm reaches satisfactory results. The algorithm can also be applied to other image style detection problems.
KeywordChinese calligraphy feature extraction deep learning calligraphy style
URLView the original
Indexed ByCPCI
Language英语
WOS Research AreaComputer Science ; Imaging Science & Photographic Technology
WOS SubjectComputer Science, Artificial Intelligence ; Imaging Science & Photographic Technology
WOS IDWOS:000414298700016
The Source to ArticleWOS
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Cited Times [WOS]:2   [WOS Record]     [Related Records in WOS]
Document TypeConference paper
CollectionUniversity of Macau
Recommended Citation
GB/T 7714
Zhang Jiulong,Guo Luming,Yang Su,et al. Detecting Chinese Calligraphy Style Consistency by Deep Learning and One-Class SVM[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2017:83-86.
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