The advent of computational and numerical methods in recent times has provided new avenues for analyzing art historiographical narratives and tracing the evolution of art styles therein. Here, we investigate an evolutionary process underpinning the emergence and stylization of contemporary user-generated visual art styles using the complexity-entropy (C-H) plane, which quantifies local structures in paintings. Informatizing 149,780 images curated in DeviantArt and Behance platforms from 2010 to 2020, we analyze the relationship between local information of the C-H space and multi-level image features generated by a deep neural network and a feature extraction algorithm. The results reveal significant statistical relationships between the C-H information of visual artistic styles and the dissimilarities of the multi-level image features over time within groups of artworks. By disclosing a particular C-H region where the diversity of image representations is noticeably manifested, our analyses reveal an empirical condition of emerging styles that are both novel in the C-H plane and characterized by greater stylistic diversity. Our research shows that visual art analyses combined with physics-inspired methodologies and machine learning, can provide macroscopic insights into quantitatively mapping relevant characteristics of an evolutionary process underpinning the creative stylization of uncharted visual arts of given groups and time.

翻译：近年来，计算与数值方法的兴起为分析艺术史学叙事及其风格演变提供了新途径。本文通过量化绘画局部结构的复杂度-熵（C-H）平面，研究当代用户生成视觉艺术风格涌现与风格化的演化过程。通过对DeviantArt和Behance平台2010至2020年间策展的149,780幅图像进行信息化处理，我们分析了C-H空间的局部信息与深度神经网络及特征提取算法生成的多层次图像特征之间的关系。研究结果表明，视觉艺术风格的C-H信息与艺术品群组内多层次图像特征的时序差异存在显著统计关联。通过揭示图像表征多样性显著呈现的特定C-H区域，我们的分析展现了新兴风格的实证条件：这些风格不仅在C-H平面中具有新颖性，且表现出更高的风格多样性。研究表明，结合物理启发方法与机器学习的视觉艺术分析，能够为定量映射特定群体与时期未知视觉艺术创造性风格化演化过程的相关特征提供宏观视角。