Scientists Show How Statistical Physics Can Reveal Art Trends Across Time and Culture

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Statistical Physics Reveals Art Trends

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Drawing the Line to Answer Art’s Big Questions

Algorithms have actually revealed that the compositional structure of Western landscape paintings altered “suspiciously” efficiently in between 1500 and 2000 ADVERTISEMENT, possibly showing a choice predisposition by art managers or in art historic literature, physicists from the Korea Advanced Institute of Science and Technology (KAIST) and associates report in the Proceedings of the National Academy of Sciences (PNAS).

KAIST analytical physicist Hawoong Jeong dealt with statisticians, digital experts and art historians in Korea, Estonia and the United States to clarify whether computer system algorithms might assist solve enduring concerns about style concepts utilized in landscape paintings, such as the positioning of the horizon and other main functions.

Algorithm Progressively Dissects Painting

Image 1. The algorithm gradually dissects the painting based upon the quantity of info in each subsequent partition. Credit: KAIST

“A foundational question among art historians is whether artwork contains organizing principles that transcend culture and time and, if yes, how these principles evolved over time,” discusses Jeong. “We developed an information-theoretic approach that can capture compositional proportion in landscape paintings and found that the preferred compositional proportion systematically evolved over time.”

Digital variations of practically 15,000 canonical landscape paintings from the Western renaissance in the 1500s to the more current modern art duration were gone through a computer system algorithm. The algorithm gradually divides art work into horizontal and vertical lines depending upon the quantity of info in each subsequent partition. It enables researchers to examine how artists and different art designs make up landscape art work, in regards to positioning of a piece’s essential elements, in addition to how high or low the landscape’s horizon is positioned.

Painting Categories Algorithm

Image 2. The paintings were divided into 4 classifications depending upon how their structures were separated by the very first 2 lines established by the algorithm. Credit: KAIST

The researchers begun by evaluating the very first 2 partitioning lines determined by the algorithm in the paintings and discovered they might be classified into 4 groups: a preliminary horizontal line followed by a 2nd horizontal line (H-H); a preliminary horizontal line followed by a 2nd vertical line (H-V); a vertical followed by horizontal line (V-H); or a vertical followed by a vertical line (V-V) (see image 1 and 2). They then took a look at the classifications with time.

They discovered that prior to the mid-nineteenth century, H-V was the dominant structure type, followed by H-H, V-H, and V-V. The mid-nineteenth century then brought modification, with the H-V structure design reducing in appeal with an increase in the H-H structure design. The other 2 designs stayed reasonably steady.

The researchers likewise took a look at how the horizon line, which separates sky from land, altered with time. In the 16th century, the dominant horizon line of the painting was above the middle of the canvas, however it slowly came down to the lower middle of the canvas by the 17th century, where it stayed till the mid-nineteenth century. After that, the horizon line started slowly increasing once again.

Interestingly, the algorithm revealed that these findings were comparable throughout cultures and creative durations, even through durations controlled by a variety in art designs. This resemblance might well be a function, then, of a predisposition in the dataset.

“In recent decades, art historians have prioritized the argument that there is great diversity in the evolution of artistic expression rather than offering a relatively smoother consensus story in Western art,” Jeong states. “This study serves as a reminder that the available large-scale datasets might be perpetuating severe biases.”

The researchers next objective to widen their analyses to consist of more varied art work, as this specific dataset was eventually Western and male prejudiced. Future analyses need to likewise think about diagonal structures in paintings, they state.

Reference: “Dissecting landscape art history with information theory” by Byunghwee Lee, Min Kyung Seo, Daniel Kim, In-seob Shin, Maximilian Schich, Hawoong Jeong and Seung Kee Han, 27 October 2020, Proceedings of the National Academy of Sciences.
DOI: 10.1073/pnas.2011927117
This work was supported by the National Research Foundation (NRF) of Korea.



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