Recognition of Yuan blue and white porcelain produced in Jingdezhen based on graph anomaly detection combining portable X-ray fluorescence spectrometry

Abstract

Abstract The blue and white porcelain produced in Jingdezhen during China’s Yuan Dynasty is an outstanding cultural heritage of ceramic art that has attracted wide attention for its identification. However, the traditional visual identification method is susceptible to misjudgment, thermoluminescence dating damages the samples, and the methods based on chemical analysis are limited by the accuracy and specificity of the elemental features. In this paper, we address the identification challenge by using machine learning techniques combined with portable X-ray Fluorescence Spectrometer (pXRF) analysis. We collect a large dataset of chemical compositions of Yuan blue and white porcelain from Jingdezhen using pXRF, and propose a graph anomaly detection method based on gradient attention map (GRAM) to identify the porcelain from different dynasties. We treat the porcelain produced in the Yuan dynasty as normal samples and those from other dynasties as abnormal samples. For GRAM, we merely train the variational graph autoencoder (VGAE) model with normal graphs and then use its encoder to extract graph features and compute the anomaly scores by utilizing the GRAM of the graph representations with respect to the node feature embeddings. Finally, we compare GRAM with state-of-the-art graph anomaly detection techniques and show that it achieves superior performance.

Keywords

• Blue and white porcelain
• Ancient ceramic identification
• Portable X-ray Fluorescence Spectrometer analysis
• Anomaly detection
• Graph neural networks