研究当代用户生成的视觉艺术的多样性和风格化在复杂度平面中的复杂性
Seunghwan Kim1, Byunghwee Lee2,3, Wonjae Lee4
1Graduate School of Culture Technology, KAIST, Daejeon, 34141, Republic of Korea.
Scientific reports
|July 2, 2025
概括
计算方法揭示了新的艺术风格是如何出现和演变的. 通过分析149,780张图像,研究人员使用复杂性分析和机器学习绘制了风格多样性图,揭示了创作新艺术的条件.
科学领域:
- 计算式的艺术史研究
- 数字人文学科 数字人文学科
- 复杂性科学是一门复杂性科学.
背景情况:
- 传统的艺术史缺乏用于风格演变的定量方法.
- 用户生成艺术的出现需要新的分析工具.
- 复杂性- (C-H) 平面量化了局部图像结构.
研究的目的:
- 研究当代视觉艺术风格的进化过程.
- 分析本地图像信息与多层次特征之间的关系.
- 确定新和多样化的艺术风格出现的条件.
主要方法:
- 分析了来自DeviantArt和Bēhance (2010-2020) 的149,780张图像.
- 利用复杂性- (C-H) 平面来量化局部结构.
- 采用深度神经网络和特征提取算法进行图像分析.
- 相关的C-H信息与多层次的图像特征随着时间的推移.
主要成果:
- 在C-H信息和图像特征不相似性之间发现了显著的统计关系.
- 确定了一个特定的C-H区域,表明图像表示的多样性很高.
- 揭示了新兴艺术风格的经验条件,其特点是新性和多样性.
- 在群体内展示了艺术风格的时间演变.
结论:
- 灵感来自物理学的方法和机器学习为艺术进化提供了宏观的见解.
- 量化分析可以绘制未知视觉艺术中创意风格化的特征.
- 这种方法为理解艺术风格的出现和多样化提供了一个框架.
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