Diversity and stylization of the contemporary user-generated visual arts in the complexity-entropy plane

Seunghwan Kim, Byunghwee Lee, Wonjae Lee
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Abstract

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.
复杂-熵平面上当代用户生成视觉艺术的多样性和风格化
近代计算和数值方法的出现为分析艺术史学叙事和追踪其中艺术风格的演变提供了新的途径。在此,我们使用量化绘画局部结构的复杂性熵(C-H)平面研究了当代用户生成的视觉艺术风格的出现和风格化的演变过程。我们利用 DeviantArt 和 Behance 平台从 2010 年到 2020 年收集的 149,780 张图片,分析了 C-H 空间的局部信息与深度神经网络和特征提取算法生成的多层次图像特征之间的关系。我们的分析通过揭示图像表现形式多样性明显体现的特定 C-H 区域,揭示了新出现的风格的经验条件,这些风格在 C-H 平面上既新颖,又具有更高的风格多样性特征。我们的研究表明,视觉艺术分析与物理学启发的方法论和机器学习相结合,可以提供宏观视角,定量绘制特定群体和时间的未知视觉艺术创意风格化进化过程的相关特征。
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