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

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。

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.