TCPVis: visual analysis system of traditional Chinese painting school based on six principles of Chinese painting

doi:10.11996/JG.j.2095-302X.2024010209

Abstract

Abstract:

Traditional Chinese painting (TCP), throughout its historical development, has formed a diverse array of painting schools. Analyzing these painting schools can greatly enhance our understanding and appreciation of the value of TCP. However, existing tools are limited in intuitively and effectively representing the distinctive stylistic features of the paintings. In response, we proposed TCPVis, a visual analysis system for multi-dimensional features analysis of painting schools in TCP based on the Six Principles of Chinese Painting. This system could automatically analyze the common features of different schools across these six principles, recommend dimension weights, and visualize the label distribution of the Six Principles dimensions to support user verification. The system also presented the collection of paintings according to the dimensionality reduction of weighted painting features, assisting users in exploring more stylistically similar paintings, and supporting the analysis of painter correlations for the paintings. The effectiveness and usability of the system in analyzing features of painting schools and exploring more painters were demonstrated through two case studies and interviews with experts.

Key words: traditional Chinese painting, visual analysis, six principles of Chinese painting, painting school analysis, digital humanities

CLC Number:

TP391

WANG Sijia, FENG Yingchaojie, ZHU Hang, ZHANG Wei, ZHU Lin, CHEN Wei. TCPVis: visual analysis system of traditional Chinese painting school based on six principles of Chinese painting[J]. Journal of Graphics, 2024, 45(1): 209-218.

Figures/Tables 6

References 31

[1]	潘天寿. 中国传统绘画的风格特点[J]. 美术, 1978(6): 16-36, 26.
	PAN T S. The style characteristics of China’s traditional paintings[J]. Art Magazine, 1978(6): 16-36, 26 (in Chinese).
[2]	蒋孔阳. 中国古代绘画的基本特点[J]. 学术月刊, 2003, 35(6): 51-57, 102.
	JIANG K Y. The essential features of traditional Chinese painting[J]. Academic Monthly, 2003, 35(6): 51-57, 102 (in Chinese).
[3]	陈佳舟, 黄可妤, 封颖超杰, 等. 基于古代绘画的古诗自动生成方法[J]. 计算机辅助设计与图形学学报, 2021, 33(7): 1038-1044.
	CHEN J Z, HUANG K Y, FENG Y, et al. Automatic poetry generation based on ancient Chinese paintings[J]. Journal of Computer-Aided Design & Computer Graphics, 2021, 33(7): 1038-1044 (in Chinese).
[4]	封颖超杰, 周姿含, 张玮, 等. “为你写诗”: 面向中国古典诗歌的可视化交互创作系统[J]. 计算机辅助设计与图形学学报, 2021, 33(9): 1318-1325.
	FENG Y, ZHOU Z H, ZHANG W, et al. WPFY: visual interactive authoring system for Chinese classical poetry[J]. Journal of Computer-Aided Design & Computer Graphics, 2021, 33(9): 1318-1325 (in Chinese).
[5]	ZHANG W, WONG J K, CHEN Y T, et al. ScrollTimes: tracing the provenance of paintings as a window into history[EB/OL]. [2023-05-09]. https://arxiv.org/abs/2306.08834.pdf.
[6]	FENG Y, CHEN J Z, HUANG K Y, et al. iPoet: interactive painting poetry creation with visual multimodal analysis[J]. Journal of Visualization, 2022, 25(3): 671-685. DOI
[7]	祝唯庸. 一看就懂的中国艺术史-三-书画卷, 盛唐盛: 世浓妆: 书画卷盛唐盛: 世浓妆[M]. 桂林: 广西师范大学出版社, 2021: 295-298.
	ZHU W Y. China art history understood at a glance- III-calligraphy and painting volume, prosperous Tang Dynasty: the world’s thick makeup[M]. Guilin, China: Guangxi Normal University Press, 2021: 295-298 (in Chinese).
[8]	蔡瑞勇. 浅谈谢赫“六法”对学习书画的影响及意义[J]. 美术教育研究, 2013(10): 10-11.
	CAI R Y. On the influence and significance of Sheikh’s “six methods” on learning calligraphy and painting[J]. Art Education Research, 2013(10): 10-11 (in Chinese).
[9]	周积寅. 中国画派论[J]. 艺术百家, 2013, 29(6): 49-70, 39.
	ZHOU J Y. Discourse upon painting school in China[J]. Hundred Schools in Arts, 2013, 29(6): 49-70, 39 (in Chinese).
[10]	SUN M J, ZHANG D, REN J C, et al. Brushstroke based sparse hybrid convolutional neural networks for author classification of Chinese ink-wash paintings[C]// 2015 IEEE International Conference on Image Processing. New York: IEEE Press, 2015: 626-630.
[11]	MENG Q, LI K, ZHOU M, et al. The Elements Extraction on Traditional Chinese Paintings Based on Object Detection[EB/OL]. (2020-02-16) [2023-05-15]. https://doi.org/10.1145/3375959.3375980.
[12]	JIANG W, WANG X Y, REN J C, et al. MTFFNet: a multi-task feature fusion framework for Chinese painting classification[J]. Cognitive Computation, 2021, 13(5): 1287-1296. DOI
[13]	BEDERSON B B. PhotoMesa: A zoomable image browser using quantum treemaps and bubblemaps[M]// The Craft of Information Visualization. Amsterdam: Elsevier, 2003: 66-75.
[14]	XIA J Z, HUANG L Q, LIN W X, et al. Interactive visual cluster analysis by contrastive dimensionality reduction[J]. IEEE Transactions on Visualization and Computer Graphics, 2023, 29(1): 734-744.
[15]	CHEN C J, WU J, WANG X H, et al. Towards better caption supervision for object detection[J]. IEEE Transactions on Visualization and Computer Graphics, 2022, 28(4): 1941-1954. DOI URL
[16]	FENG Y, WANG X, WONG K K, et al. PromptMagician: Interactive Prompt Engineering for Text-to-Image Creation[EB/OL]. (2023-08-15) [2023-05-15]. https://doi.org/10.48550/arXiv.2307.09036.
[17]	ESPADOTO M, MARTINS R M, KERREN A, et al. Toward a quantitative survey of dimension reduction techniques[J]. IEEE Transactions on Visualization and Computer Graphics, 2021, 27(3): 2153-2173. DOI URL
[18]	VAN DER MAATEN L, POSTMA E, HERIK J. Dimensionality reduction: a comparative review[EB/OL]. [2023-05-09]. https://www.researchgate.net/publication/228657549_Dimensionality_Reduction_A_Comparative_Review.
[19]	ROWEIS S T, SAUL L K. Nonlinear dimensionality reduction by locally linear embedding[J]. Science, 2000, 290(5500): 2323-2326. DOI PMID
[20]	TENENBAUM J B, DE SILVA V, LANGFORD J C. A global geometric framework for nonlinear dimensionality reduction[J]. Science, 2000, 290(5500): 2319-2323. DOI PMID
[21]	MCINNES L, HEALY J, MELVILLE J. UMAP: uniform manifold approximation and projection for dimension reduction[EB/OL]. [2023-05-09]. https://arxiv.org/abs/1802.03426.pdf
[22]	VAN DER MAATEN L, HINTON G. Visualizing data using t-SNE[J]. Journal of machine learning research, 2008, 9(11): 2579-2605.
[23]	ZHANG W, ZHANG J W, WONG K K, et al. Computational Approaches for Traditional Chinese Painting: From the “Six Principles of Painting” Perspective[EB/OL]. (2023-07-26) [2023-10-15]. https://doi.org/10.48550/arXiv.2307.14227.
[24]	GUO F, TANG J, PENG H. Image recovery for ancient Chinese paintings[J]. International Journal of Signal Processing, Image Processing and Pattern Recognition, 2013, 6(5): 165-178. DOI URL
[25]	张佳婧, 于金辉, 缪永伟, 等. 基于深度学习的自适应水墨画计算美学评估[J]. 计算机辅助设计与图形学学报, 2021, 33(9): 1349-1360.
	ZHANG J J, YU J H, MIAO Y W, et al. Self-adaptive computational aesthetic evaluation of Chinese ink paintings based on deep learning[J]. Journal of Computer-Aided Design & Computer Graphics, 2021, 33(9): 1349-1360 (in Chinese).
[26]	LIANG H, YANG S, WANG W, et al. Instance-aware coherent videostyle transfer for Chinese ink wash painting[EB/OL]. (2021-08-01) [2023-05-15]. https://doi.org/10.24963/ijcai.2021/114.
[27]	李锦胜. 《二十四画品》刍议[J]. 文艺理论与批评, 1999(2): 122-125.
	LI J S. On twenty-four paintings[J]. Theory and Criticism of Literature and Art, 1999(2): 122-125 (in Chinese).
[28]	杨宇. 多指标综合评价中赋权方法评析[J]. 统计与决策, 2006(13): 17-19.
	YANG Y. Evaluation of weighting methods in multi-index comprehensive evaluation[J]. Statistics & Decision, 2006(13): 17-19 (in Chinese).
[29]	吴群燕. “湖州竹派”研究[D]. 上海: 上海大学, 2015.
	WU Q Y. Research on Huzhou bamboo painting school[D]. Shanghai: Shanghai University, 2015 (in Chinese).
[30]	顾平. 皇家赞助与文化认同: 南宋院体山水画风格研究[D]. 南京: 南京艺术学院, 2002.
	GU P. Royal sponsorship and cultural identity— study on the style of courtyard landscape painting in southern song dynasty[D]. Nanjing: Nanjing University of the Arts, 2002 (in Chinese).
[31]	孙文忠. 从郭熙到马、夏: 宋代画院山水画审美特色管窥[J]. 美术研究, 2004(1): 75-78.
	SUN W Z. From Guo xi to ma Xia—on the aesthetic characteristics of landscape paintings in song dynasty painting academy[J]. Art Research Arch, 2004(1): 75-78 (in Chinese).

特征	描述	详细标签
主题	国画的画科	山水、花鸟、人物(共3个)
意境	画作在情感、思想等方面所展现出的深远意义	韶秀、沉雄、空灵等^[27] (共24个)
技法	画作中使用的具有代表性的技法	披麻皴、铁线描等(共43个)
物象	画作中包含的物象	叶、鸟、花、果等(共56个)
构图	画作的构图特点	两线构图、中心构图等(共26个)
赋彩	画作的色彩特征信息	重彩、水墨、白描等(共13个)

特征	描述	详细标签
主题	国画的画科	山水、花鸟、人物(共3个)
意境	画作在情感、思想等方面所展现出的深远意义	韶秀、沉雄、空灵等^[27] (共24个)
技法	画作中使用的具有代表性的技法	披麻皴、铁线描等(共43个)
物象	画作中包含的物象	叶、鸟、花、果等(共56个)
构图	画作的构图特点	两线构图、中心构图等(共26个)
赋彩	画作的色彩特征信息	重彩、水墨、白描等(共13个)