Abstract
A physical process is characterized as complex when it is difficult to analyze and explain in a simple way, and even more difficult to predict. The complexity within an art painting is expected to be high, possibly comparable to that of nature. Herein, we apply a 2D stochastic methodology to images of both portrait photography and artistic portraits, the latter belonging to different genres of art, with the aim to better understand their variability in quantitative terms. To quantify the dependence structure and variability, we estimate the Hurst parameter, which is a common dependence metric for hydrometeorological processes. We also seek connections between the identified stochastic patterns and the desideratum that each art movement aimed to express. Results show remarkable stochastic similarities between portrait paintings, linked to philosophical, cultural and theological characteristics of each period.
Highlights
The aesthetic evaluation of art paintings typically involves the physical procedure of visual examination of an image [1,2,3,4,5,6,7,8,9]
In an attempt to conceive an alternative and objective procedure for the aesthetic analysis, artificial intelligence and mathematical tools are the subject of a plethora of related publications [11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44]
A strong dependence structure is observed in the portraits of 20th century and Picasso’s self-portraits, yet with a bigger range of fluctuations, indicative of the artistically freer and nonrepresentational approach of modern art
Summary
The aesthetic evaluation of art paintings typically involves the physical procedure of visual examination of an image [1,2,3,4,5,6,7,8,9]. Under the viewpoint of stochastics, there is no such thing as a virus of randomness that infects some phenomena to make them random, leaving other phenomena unaffected Rather, it seems that both randomness and predictability coexist and are intrinsic to natural systems, which can be deterministic and random at the same time, depending on the prediction horizon and the time scale [48]. By applying the methods of stochastics, we can quantify this uncertainty treating the unpredictable fluctuations of art works as realizations of complex stochastic processes
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