一个进化优化的卷积神经网络的框架,用于对上朝和朝的分类,青铜装饰图案
XiuZhi Qi1,2, XueMei He1, Shan Wei Chen3,4
1College of Art and Design, Shaanxi University of Science & Technology, Xi'an, China.
PloS one
|May 14, 2024
概括
一种新方法通过使用与差异进化优化的卷积神经网络 (CNN) 来对古代中国青铜器件的装饰图案进行分类. 这个香铜CNN (SCB-CNN) 提高了数字保存的分类准确性和速度.
科学领域:
- 文化遗产的数字化保护.
- 人工智能在艺术史中的应用.
- 在历史文物中的模式识别.
背景情况:
- 来自和王朝的青铜文物是联合国教科文组织世界遗产,具有重要的美学和文化价值.
- 对这些文物的装饰图案进行分类对于其数字保存和保护至关重要.
- 现有的方法可能对复杂模式分类无效.
研究的目的:
- 提出一种有效的方法来分类香和铜器件 (SCB) 上的装饰图案.
- 开发一个用于SCB模式分类的优化卷积神经网络 (CNN) 模型.
- 加强SCB文物的数字保存和研究.
主要方法:
- 收集了来自香格里拉和族的青铜文物的原始装饰图案.
- 使用图像增强技术扩展了数据集.
- 根据经典的CNN结构开发了一个Shang和Chow青铜卷积神经网络 (SCB-CNN).
- 使用差异演化算法优化了SCB-CNN的初始参数.
- 对未经优化模型,VGG-Net和Google.Net进行了比较实验.
主要成果:
- 优化后的SCB-CNN显著减少了训练时间.
- 央行-CNN保持了快速的预测和趋同速度.
- 在分类SCB装饰图案方面取得了很高的准确性.
- 对比实验验证实了优化SCB-CNN的优越性能.
结论:
- 优化的SCB-CNN提供了一种高效准确的解决方案,用于对和青铜器件的装饰图案进行分类.
- 这种方法有助于数字化保存和保护宝贵的文化遗产.
- 该研究为SCB模式的继承和创新提供了新的见解.
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