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An effective method to detect and categorize digitized traditional Chinese paintings
Jiang, SQ; Huang, QM; Ye, QX; Gao, W
2006-05-01
发表期刊PATTERN RECOGNITION LETTERS
ISSN0167-8655
卷号27期号:7页码:734-746
摘要Traditional Chinese painting (TCP) is the gem of Chinese traditional arts. More and more TCP images are digitized and exhibited on the Internet. Effectively browsing and retrieving them is an important problem that needs to be addressed. Gongbi (traditional Chinese realistic painting) and Xieyi (freehand style) are two basic types of traditional Chinese paintings. This paper proposes a scheme to detect TCPs from general images and categorize them into Gongbi and Xieyi schools. Low-level features such as color histogram, color coherence vectors, autocorrelation texture features and the newly proposed edge-size histogram are used to achieve the high-level classification. Support vector machine (SVM) is applied as the main classifier to obtain satisfactory classification results. Experimental results show the effectiveness of the method. (c) 2005 Elsevier B.V. All rights reserved.
关键词traditional Chinese painting image classification edge-size histogram
DOI10.1016/j.patrec.2005.10.017
收录类别SCI
语种英语
WOS研究方向Computer Science
WOS类目Computer Science, Artificial Intelligence
WOS记录号WOS:000236631000005
出版者ELSEVIER SCIENCE BV
引用统计
被引频次:49[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://119.78.100.204/handle/2XEOYT63/10454
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Jiang, SQ
作者单位1.Chinese Acad Sci, Comp Technol Inst, Beijing 100080, Peoples R China
2.Chinese Acad Sci, Grad Sch, Beijing 100039, Peoples R China
推荐引用方式
GB/T 7714
Jiang, SQ,Huang, QM,Ye, QX,et al. An effective method to detect and categorize digitized traditional Chinese paintings[J]. PATTERN RECOGNITION LETTERS,2006,27(7):734-746.
APA Jiang, SQ,Huang, QM,Ye, QX,&Gao, W.(2006).An effective method to detect and categorize digitized traditional Chinese paintings.PATTERN RECOGNITION LETTERS,27(7),734-746.
MLA Jiang, SQ,et al."An effective method to detect and categorize digitized traditional Chinese paintings".PATTERN RECOGNITION LETTERS 27.7(2006):734-746.
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