Scholarlink

<p>The chronological classification of Jingdezhen blue and white porcelain in past dynasties has high academic and socioeconomic research value. In this study, RGB, non-imaging spectral, and imaging spectral datasets of Jingdezhen blue and white porcelain were constructed with small samples, and the spectral range was between 350-950 nm. The transfer learning classification and identification model of blue and white porcelain was established based on the VGG16 and ResNet50 models. On this basis, Dempster-Shafer (DS) evidence theory was used to construct a chronological classification and identification model of blue and white porcelain imaging spectral data that combined spatial and spectral information. The experimental comparison shows that in the case of small samples, the method incorporating the ResNet50 network model and stepwise discriminant analysis combined with the Long Short-Term Memory (LSTM) algorithm had the best classification effect, and the classification accuracy reached 90.47%. This study provides a new method and idea for the nondestructive identification and classification of cultural relics.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Dating of Jingdezhen blue and white porcelain based on transfer learning and imaging spectroscopy techniques

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年3月11日

查看全文
  • Hengqian Zhao,
  • Guanglong Tang,
  • Zhiheng Hu,
  • Yu Xie,
  • Ge Liu,
  • Zhengpu Lu,
  • Pan Wang

摘要

The chronological classification of Jingdezhen blue and white porcelain in past dynasties has high academic and socioeconomic research value. In this study, RGB, non-imaging spectral, and imaging spectral datasets of Jingdezhen blue and white porcelain were constructed with small samples, and the spectral range was between 350-950 nm. The transfer learning classification and identification model of blue and white porcelain was established based on the VGG16 and ResNet50 models. On this basis, Dempster-Shafer (DS) evidence theory was used to construct a chronological classification and identification model of blue and white porcelain imaging spectral data that combined spatial and spectral information. The experimental comparison shows that in the case of small samples, the method incorporating the ResNet50 network model and stepwise discriminant analysis combined with the Long Short-Term Memory (LSTM) algorithm had the best classification effect, and the classification accuracy reached 90.47%. This study provides a new method and idea for the nondestructive identification and classification of cultural relics.