基于颜色特征的画家艺术风格提取方法

doi:10.11772/j.issn.1001-9081.2019111886

计算机应用 ›› 2020, Vol. 40 ›› Issue (6): 1818-1823.DOI: 10.11772/j.issn.1001-9081.2019111886

• 虚拟现实与多媒体计算 • 上一篇下一篇

基于颜色特征的画家艺术风格提取方法

王栖榕, 黄樟灿

武汉理工大学理学院，武汉 430070

收稿日期:2019-11-06 修回日期:2019-12-23 出版日期:2020-06-10 发布日期:2020-06-18
通讯作者: 黄樟灿(1960—)
作者简介:王栖榕(1995-)，女，山西晋中人，硕士研究生，主要研究方向：图像处理.黄樟灿(1960-)，男，浙江绍兴人，教授，博士生导师，博士，主要研究方向：智能计算、图像处理.
基金资助:
国家自然科学基金青年科学基金资助项目（61702388）。

Painter artistic style extraction method based on color features

WANG Qirong, HUANG Zhangcan

School of Science, Wuhan University of Technology, Wuhan Hubei 430070, China

Received:2019-11-06 Revised:2019-12-23 Online:2020-06-10 Published:2020-06-18
Contact: HUANG Zhangcan,born in 1960,Ph. D.,professor. His research interests include intelligent computing,image processing.
About author:WANG Qirong，born in 1995，M. S. candidate. Her research interests include image processing.HUANG Zhangcan，born in 1960，Ph. D.，professor. His research interests include intelligent computing，image processing.
Supported by:
Youth Program of National Natural Science Foundation of China (61702388).

摘要/Abstract

摘要： 针对现有全局与局部特征提取方法所提取的颜色特征无法有效描述画家艺术风格的问题，提出了一种基于关键区域的油画描述法。首先，通过融合主色调占比与颜色丰富度定义了油画区域信息丰富度，以检测并选取油画的关键区域。其次，提取所选油画关键区域的54维特征来描述油画，并利用Fisher Score对这些特征进行评估，选取重要的特征描述油画关键区域，从而高度体现画家艺术风格。最后，为了验证上述方法的有效性，利用朴素贝叶斯分类器实现油画的分类。建立两个数据库进行仿真实验，数据库1包含3位画家的120幅作品，数据库2包含9位画家3种流派的513幅作品。数据库1上的实验结果表明，结合Fisher Score特征描述的分类正确率高于普通分类的正确率，所提方法依据画家与流派的油画分类正确率分别达到了90%与90.20%。数据库2上的实验结果表明，所提方法根据画家的油画分类正确率达到了90%，相较Features-FS的正确率提高了6.67个百分点；依据流派分类的结果正确率达到了90.20%，与Features-FS的正确率相当。所提的基于关键区域的油画描述法所提取的特征能够有效描述画家的艺术风格。

关键词: 油画分类, 关键区域, 信息丰富度, 颜色风格, Fisher Score

Abstract: Since the ineffectiveness of color features extracted by global and local feature extraction methods to describe the artistic style of painter, a new oil painting description method based on key region was proposed. Firstly, the information richness of oil painting region was defined by incorporating the proportion of primary color and color diversity to detect and select the key region of an oil painting. Secondly, the color features in 54 dimensions of the selected key region were used to describe the oil painting, those features were evaluated by Fisher Score, and the important features were selected to describe the key region of the oil painting, so as to highly reflect the painter artistic style. Finally, to verify the validity of the proposed method, the Naive Bayes classifier was used to realize oil painting classification. Two databases were established to perform simulation experiments. The database 1 includes 120 oil paintings by three painters, and the database 2 includes 513 oil paintings by nine painters from three different schools. The experimental results on database 1 show that, the accuracy of classification combined with Fisher Score is higher than the accuracy of ordinary classification, the accuracy of the proposed method for classifying oil paintings according to painter and school is 90% and 90.20% respectively. The experimental results on database 2 show that the accuracy of the proposed method for classifying oil paintings according to painter reaches 90%, which is 6.67 percentage points higher than that of Feature selected by Fisher Score(Features-FS), and the accuracy of the proposed method for classifying oil paintings according to school is 90.20%, which is comparable to that of Features-FS. The features extracted by the proposed oil painting description method based on key region can effectively describe the artistic style of painter.

Key words: oil painting classification, key region, information richness, color style, Fisher Score

中图分类号:

TP391.41

王栖榕, 黄樟灿. 基于颜色特征的画家艺术风格提取方法[J]. 计算机应用, 2020, 40(6): 1818-1823.

WANG Qirong, HUANG Zhangcan. Painter artistic style extraction method based on color features[J]. Journal of Computer Applications, 2020, 40(6): 1818-1823.

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基于颜色特征的画家艺术风格提取方法

Painter artistic style extraction method based on color features

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