绘画艺术图像的计算美学:研究前沿与展望
Computational Aesthetics of Fine Art Paintings: The State of the Art and Outlook
查看参考文献142篇
|
文摘
|
绘画艺术是人类艺术创作的重要组成部分,绘画艺术图像的计算美学是利用机器实现可计算的人类审美过程,其在大规模绘画的自动化分析和机器对感性的计算建模上具有重要的应用价值和科学意义.针对其交叉学科的特点,本文首次从人类审美的感知、认知和评价三个关键过程出发,将绘画艺术图像的计算美学研究完整地归纳为属性识别、内容理解和美学评价三方面研究内容,对其中的问题建模、数据获取和前沿方法等关键科学问题进行了归纳总结,并对绘画计算美学的三方面研究内容进行了对比、思考和展望. |
|
其他语种文摘
|
Fine art painting is an essential component of art. The computational aesthetics of fine art painting is a computable human aesthetic process realized by machines, which has significant application value and scientific significance in the automatic analysis of large-scale paintings and computational modeling for aesthetic. Given its interdisciplinary characteristics, for the first time, the computational aesthetics of fine art paintings is completely summarized into three aspects: Attribute recognition, content understanding, and aesthetic judgments according to the key processes of human aesthetics that include perception, cognition, and evaluation. The key scientific issues involved in each aspect are summarized, such as problem modeling, data acquisition, and frontier methods. Also, the three research contents of computational aesthetics of fine art painting are compared, and the future development of this field is discussed. |
|
来源
|
自动化学报
,2020,46(11):2239-2259 【核心库】
|
|
DOI
|
10.16383/j.aas.c200358
|
|
关键词
|
绘画艺术
;
计算美学
;
审美模型
;
属性识别
;
内容理解
;
美学评价
|
|
地址
|
1.
中国科学院自动化研究所, 复杂系统管理与控制国家重点实验室, 北京, 100190
2.
中国科学院大学人工智能学院, 北京, 100049
3.
中央美术学院, 北京, 100105
|
|
语种
|
中文 |
|
文献类型
|
综述型 |
|
ISSN
|
0254-4156 |
|
学科
|
自动化技术、计算机技术 |
|
文献收藏号
|
CSCD:6844824
|
参考文献 共
142
共8页
|
|
1.
Zangwill N. The concept of the aesthetic.
European Journal of Philosophy,1998,6(1):78-93
|
被引
1
次
|
|
|
|
|
2.
Fechner G T.
Vorschule der Aesthetik,1876:1-7
|
被引
1
次
|
|
|
|
|
3.
Hoenig F. Defining computational aesthetics.
Proceedings of the 1st Eurographics Conference on Computational Aesthetics in Graphics, Visualization and Imaging,2005:13-18
|
被引
3
次
|
|
|
|
|
4.
Leder H. A model of aesthetic appreciation and aesthetic judgments.
British Journal of Psychology,2004,95(4):489-508
|
被引
16
次
|
|
|
|
|
5.
Newman M E J. Scientific collaboration networks. I. Network construction and fundamental results.
Physical Review E,2001,64(1):016131
|
被引
411
次
|
|
|
|
|
6.
Bastian M. Gephi: An open source software for exploring and manipulating networks.
Proceedings of the 3rd International Conference on Weblogs and Social Media,2009
|
被引
2
次
|
|
|
|
|
7.
王伟凝. 可计算图像美学研究进展.
中国图象图形学报,2012,17(8):893-901
|
被引
14
次
|
|
|
|
|
8.
白茹意. 绘画图像美学研究方法综述.
中国图象图形学报,2019,24(11):1860-1881
|
被引
8
次
|
|
|
|
|
9.
Fiorucci M. Machine learning for cultural heritage: A survey.
Pattern Recognition Letters,2020,133:102-108
|
被引
1
次
|
|
|
|
|
10.
Perc M. Beauty in artistic expressions through the eyes of networks and physics.
Journal of The Royal Society Interface,2020,17(164):20190686
|
被引
1
次
|
|
|
|
|
11.
Liu C. Classification of traditional Chinese paintings based on supervised learning methods.
Proceedings of the 2014 IEEE International Conference on Signal Processing, Communications and Computing,2014:641-644
|
被引
1
次
|
|
|
|
|
12.
Yu H. Color texture moments for content-based image retrieval.
Proceedings of the 2002 International Conference on Image Processing,2002:929-932
|
被引
2
次
|
|
|
|
|
13.
Jiang S Q. An effective method to detect and categorize digitized traditional Chinese paintings.
Pattern Recognition Letters,2006,27(7):734-746
|
被引
14
次
|
|
|
|
|
14.
Manjunath B S. Color and texture descriptors.
IEEE Transactions on Circuits and Systems for Video Technology,2001,11(6):703-715
|
被引
95
次
|
|
|
|
|
15.
Khan R. Discriminative color descriptors.
Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition,2013:2866-2873
|
被引
1
次
|
|
|
|
|
16.
Falomir Z. Categorizing paintings in art styles based on qualitative color descriptors, quantitative global features and machine learning (QArt-Learn).
Expert Systems with Applications,2018,97:83-94
|
被引
2
次
|
|
|
|
|
17.
Lu G M. Content-based identifying and classifying traditional chinese painting images.
Proceedings of the 2008 Congress on Image and Signal Processing,2008:570-2574
|
被引
1
次
|
|
|
|
|
18.
Haralick R M. Statistical and structural approaches to texture.
Proceedings of the IEEE,1979,67(5):786-804
|
被引
228
次
|
|
|
|
|
19.
Gatys L A. Image style transfer using convolutional neural networks.
Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition,2016:2414-2423
|
被引
10
次
|
|
|
|
|
20.
Zhong S H. Fine-art painting classification via two-channel dual path networks.
International Journal of Machine Learning and Cybernetics,2020,11(1):137-152
|
被引
1
次
|
|
|
|
|
了解气象卫星更多信息，请访问