Departamento de Física, Universidade Estadual de Maringá, Maringá, PR 87020-900, Brazil.
Faculty of Natural Sciences and Mathematics, University of Maribor, 2000 Maribor, Slovenia;
Proc Natl Acad Sci U S A. 2018 Sep 11;115(37):E8585-E8594. doi: 10.1073/pnas.1800083115. Epub 2018 Aug 27.
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 140,000 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 the applied complexity-entropy measures can be used for an effective classification of artworks.
艺术是人类创造力的终极表现，深受相应历史时期哲学和文化的影响。因此，对艺术进行定量分析对于更好地理解人类文化进化至关重要。在这里，我们对近 140000 幅画作进行了大规模的定量分析，这些画作跨越了近千年的艺术史。基于这些画作图像中的局部空间模式，我们估计了排列熵和统计复杂度。这些度量将艺术品的视觉有序程度映射到一个秩序-无序和简单-复杂的尺度上，局部反映了艺术史学家提出的定性类别。这些度量的动态行为揭示了艺术的明显时间演变，其转折点与艺术的主要历史时期一致。我们的研究表明，不同的艺术风格具有明显不同的平均熵和复杂度，因此可以根据这些度量标准对风格进行分层组织和聚类。我们还进一步验证了所识别的组与用于定性描述风格的文本内容很好地对应，并且所应用的复杂度-熵度量可以有效地对艺术品进行分类。
