What question did this study set out to answer?

The central aim is to classify and interpret blue-and-white porcelain based on dynastic periods using deep learning.

April 17, 2026Open Access

Multi-view dynastic classification and visual interpretation of blue-and-white porcelain

Key Points

The central aim is to classify and interpret blue-and-white porcelain based on dynastic periods using deep learning.
Curated a multi-view dataset of Ming and Qing porcelain with 284 objects and 963 images.
Employed a pretrained and fine-tuned ResNet-50 model for classification.
Incorporated multiple object views during training to enhance model performance.
Utilized visual explanation techniques like Grad-CAM and t-SNE for feature interpretation.
The ResNet-50 model achieved the highest classification performance among evaluated models.
Incorporating multiple views significantly improved classification accuracy compared to single view training.
Visual explanation techniques revealed shape and motifs as key characteristics.
t-SNE visualizations indicated global clustering by dynasty and local clustering by object.

Abstract

This study applies deep learning to classify—and interpret—blue-and-white porcelain images by dynastic period and studies the role of multiple object views. A multi-view dataset of Ming and Qing porcelain captured from different angles was curated, resulting in 284 objects and 963 images. Among the evaluated models, a pretrained and fine-tuned ResNet-50 (ImageNet1K-V2) achieved the best performance. Incorporating multiple views of the same object during training improved classification performance compared with a single view. Visual explanation techniques (Grad-CAM, Ablation-CAM, Score-CAM) highlighted shape and motifs as characteristic features. t-SNE visualisation showed learned features cluster by dynasty globally and object locally.

Multi-view dynastic classification and visual interpretation of blue-and-white porcelain

Key Points

Abstract

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