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