History of art paintings through the lens of entropy and complexity

TL;DR

This study quantitatively analyzes nearly 140,000 paintings over a millennium using entropy and complexity measures to reveal historical and stylistic evolution in art, aligning with art historical categories.

Contribution

Introduces a large-scale, quantitative approach to analyze art history through entropy and complexity metrics, enabling classification and understanding of stylistic evolution.

Findings

Distinct entropy and complexity profiles for different art styles

Temporal evolution of art aligns with historical periods

Effective classification of artworks based on complexity-entropy measures

Abstract

Art is the ultimate expression of human creativity that is deeply influenced by the philosophy and culture of the corresponding historical epoch. The quantitative analysis of art is therefore essential for better understanding human cultural evolution. Here we present a large-scale quantitative analysis of almost 140 thousand paintings, spanning nearly a millennium of art history. Based on the local spatial patterns in the images of these paintings, we estimate the permutation entropy and the statistical complexity of each painting. These measures map the degree of visual order of artworks into a scale of order-disorder and simplicity-complexity that locally reflects qualitative categories proposed by art historians. The dynamical behavior of these measures reveals a clear temporal evolution of art, marked by transitions that agree with the main historical periods of art. Our research shows that different artistic styles have a distinct average degree of entropy and complexity, thus allowing a hierarchical organization and clustering of styles according to these metrics. We have further verified that the identified groups correspond well with the textual content used to qualitatively describe the styles, and that the employed complexity-entropy measures can be used for an effective classification of artworks.