基于马尔可夫链的书画时序感量化方法

doi:10.11772/j.issn.1001-9081.2020061004

计算机应用 ›› 2021, Vol. 41 ›› Issue (1): 295-299.DOI: 10.11772/j.issn.1001-9081.2020061004

所属专题：前沿与综合应用

基于马尔可夫链的书画时序感量化方法

律睿慜^1,2, 梅莉琳^1,2, 邢红姹^1,2, 孟磊³, 昃跃峰³

1. 江南大学人工智能与计算机学院, 江苏无锡 214122;
2. 江苏省媒体设计与软件技术重点实验室(江南大学), 江苏无锡 214122;
3. 江南大学设计学院, 江苏无锡 214122

收稿日期:2020-07-10 修回日期:2020-10-25 出版日期:2021-01-10 发布日期:2021-01-16
通讯作者: 梅莉琳
作者简介:律睿慜(1982-),男,四川成都人,副教授,博士,CCF会员,主要研究方向:书画与科技的关联性;梅莉琳(1994-),女,江苏常州人,硕士研究生,主要研究方向:面向书画的软件设计与开发;邢红姹(1996-),女,河北邢台人,硕士研究生,主要研究方向:面向书画的软件设计与开发;孟磊(1984-),男,山东青州人,副教授,博士,主要研究方向:数字语境下文化遗产传播系统设计;昃跃峰(1983-),男,山东济南人,讲师,博士,主要研究方向:中国文人美学、数字媒体艺术。
基金资助:
江南文化和大运河文化带建设研究重点课题项目（JUSRP12089）；教育部人文社会科学研究青年基金资助项目（18YJC760123）；江苏省媒体设计与软件技术重点实验室开放课题（19ST0101）。

Sequentiality perception quantification method of painting and calligraphy based on Markov chain

LYU Ruimin^1,2, MEI Lilin^1,2, XING Hongcha^1,2, MENG Lei³, ZE Yuefeng³

1. School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi Jiangsu 214122, China;
2. Jiangsu Key Laboratory of Media Design and Software Technology(Jiangnan University), Wuxi Jiangsu 214122, China;
3. School of Design, Jiangnan University, Wuxi Jiangsu 214122, China

Received:2020-07-10 Revised:2020-10-25 Online:2021-01-10 Published:2021-01-16
Supported by:
This work is partially supported by the Key Project of Jiangnan Culture and the Grand Canal Cultural Belt Construction (JUSRP12089), the Humanities and Social Sciences Youth Foundation of Ministry of Education of China (18YJC760123), the Open Project of Jiangsu Key Laboratory of Media Design and Software Technology (19ST0101).

摘要/Abstract

摘要： 书法欣赏被广泛认为需要进行时序还原，而绘画的时序还原长期被忽略，并且笔触的细节特征被认为能增强时序的感知。为了量化时序感以及探究笔触细节特征对时序感的影响，提出了一种基于马尔可夫链熵率的书画作品时序感量化方法。首先，将个体在书画作品上标记点的感知时序建模为马尔可夫链；然后，计算马尔可夫模型的熵率得到感知时序的不确定性；最后，采用负熵来衡量感知时序的有序性，并将其归一化得到量化指标——时序感。通过对多个书画作品的时序感实测，验证了此方法的可行性，并基于该度量研究了图形变换对书画作品时序感知的影响。实验结果显示，原始图像在旋转或镜像变换后的时序感的一致性保持在较高水平，但正确率有显著变化。这意味着，笔触特征并非形成时序感受的首要因素，观者自身的笔顺经验在其中更加重要，而该推论还需进一步验证。

关键词: 时序, 书画, 审美, 马尔可夫链, 熵率

Abstract: Calligraphy appreciation is widely considered to require sequence restoration, while the sequence restoration of painting is ignored in long time. Moreover, the detail feature of brush strokes is considered to enhance the perception of sequentiality. In order to quantify the sequentiality perception and to explore the influence of detail features of strokes on sequentiality perception, a sequentiality perception quantization method based on Markov chain entropy rate was proposed. Firstly, the perceived sequentiality of an individual to the markers on the artwork was modeled as a Markov chain. Then, the entropy rate of the Markov model was calculated to measure the uncertainty of the perceived sequentiality. Finally, the negentropy was used to measure the order of the perceived sequentiality and was normalized to obtain the measurement index:sequentiality perception. The feasibility of this method was verified through the actual measurement of the sequentiality perception of a group of artworks. And based on the proposed sequentiality measurement, the effect of graph transformation on the sequentiality perception of artworks was studied. Experimental results show that the sequentiality consistency keeps high level when rotation or mirror transformation is performed to the original image, while the correctness varies significantly. This means that the feature of brush strokes is not the primary factor in forming the sequentiality experience, and the viewer's own experience of order of strokes is more important in the formation, but this conclusion needs to be further verified.

Key words: sequentiality, painting and calligraphy, aesthetics, Markov chain, entropy rate

中图分类号:

TP37

律睿慜, 梅莉琳, 邢红姹, 孟磊, 昃跃峰. 基于马尔可夫链的书画时序感量化方法[J]. 计算机应用, 2021, 41(1): 295-299.

LYU Ruimin, MEI Lilin, XING Hongcha, MENG Lei, ZE Yuefeng. Sequentiality perception quantification method of painting and calligraphy based on Markov chain[J]. Journal of Computer Applications, 2021, 41(1): 295-299.

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基于马尔可夫链的书画时序感量化方法

Sequentiality perception quantification method of painting and calligraphy based on Markov chain

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