基于线条形状和主方向墨色分布特征的中国画画家识别算法<sup>*</sup>

doi:10.16451/j.cnki.issn1003-6059.201710006

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Abstract Most of the existing Chinese paintings research focuses on the content rather than the artistic style. Since the essence of Chinese paintings is represented by brushwork and ink, different artists can be normally identified by the style of their brushwork. In this paper, an algorithm is proposed to classify Chinese paintings based on brushwork and ink. The line shape feature and the ink color distribution feature are described. Combining these two features, a complex character is established. And the complex character is used as the input of the support vector machines classifier. Extensive experiments show that the average recall and precision of the proposed algorithm are higher than those of the representative existing benchmarks, including MHMM, C4.5 and Fusion. The proposed algorithm can be used for the digital analysis, management, understanding and identification of Chinese paintings. Moreover, it provides an effective digital tool for the inheritance and appreciation of Chinese painting.

Key words： Classification of Chinese Paintings Histogram of Oriented Gradient Ink Color of Principal Direction Line Shape Feature Extraction

Received: 27 March 2017

ZTFLH:

TP 391.41

Fund:Supported by National Natural Science Foundation of China(No.61502331), Natural Science Foundation of Tianjin(No.15JCQNJC
00800,16JCYBJC42000), Open Project of Information Technology Research Base of Civil Aviation Administration of China(No.CAAC-ITRB-201504).

About author:: (LIU Shang, born in 1977, Ph.D., asso-ciate professor. Her research interests include pattern recognition and image processing.)
(SHENG Jiachuan(Corresponding author), born in 1982, Ph.D., associate professor. Her research interests include pattern recognition and multi-media processing.)

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Cite this article:

LIU Shang,SHENG Jiachuan. Artists Recognition via Line Shape and Ink Color Distribution of the Principal Direction for Chinese Paintings[J]. , 2017, 30(10): 917-927.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201710006 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2017/V30/I10/917

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