使用集体学习来分类传统中国木雕中的艺术风格
1School of Art and Design, Weifang University of Science and Technology, 262700, Shouguang, China.
Scientific reports
|November 13, 2025
概括
这项研究引入了一种自动化方法,用于使用混合卷积神经网络 (CNN) 和分类和回归树 (CART) 模型对中国木雕艺术风格进行分类,提高了4.7%的准确性.
科学领域:
- 艺术史 艺术史 艺术史
- 计算机视觉 计算机视觉
- 数字人文学科 数字人文学科
背景情况:
- 中国的木雕艺术拥有丰富的历史,各种风格和技术.
- 现有的方法缺乏自动化方法来识别和分类木雕艺术风格和时期.
研究的目的:
- 开发一种自动化,混合的方法来识别和分类中国木雕艺术风格和时间段.
- 通过计算方法提高对中国艺术的科学和应用理解.
主要方法:
- 图像编译,预处理和颜色转换用于特征提取.
- 一个使用卷积神经网络 (CNN) 进行初始预测的集合模型.
- 一种混合方法,将CNN预测与分类和回归树 (CART) 的元模型相结合,以提高准确性.
主要成果:
- 拟议的混合方法在中国木切分类的准确度 (4.7%) 和精度 (4%) 中取得了显著的改进.
- 在识别艺术风格和时间段方面表现出高水平的准确性和精确性.
- 在分类任务中表现优于现有的比较方法.
结论:
- 开发的多阶段方法为自动识别和分类中国木刻提供了有效的解决方案.
- 这种方法有助于中国艺术的科学研究和实际应用的进步.
- 该模型准确有效地区分不同的艺术风格和木雕艺术的历史时期.
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