Pottery evolution pattern discovery based on deep learning: case study of Miaozigou culture in China

• Published in: Heritage science Vol. 12; no. 1; pp. 352 - 13
• Main Authors: Pang, Honglin, Qi, Xiujin, Xiao, Chengjun, Xu, Ziying, Ding, Guangchen, Chang, Yi, Yang, Xi, Duan, Tianjing
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 10.10.2024; Springer Nature B.V; SpringerOpen
• Subjects: Algorithms; Artifact identification; Artificial neural networks; Ceramics; Chemistry and Materials Science; Clustering; Culture; Deep Learning; Digital imaging; Evolution; Historic sites; Machine learning; Materials Science; Neural networks; Potteries evolution; Pottery; Clustering; Potteries evolution; Deep Learning
• ISSN: 2050-7445; 2050-7445
• DOI: 10.1186/s40494-024-01468-y

Potteries, one of the tools widely used by early humans, encapsulates rich historical information. Deep neural networks have been applied to analyzing pottery digital images, bypassing the need for intricate handcrafted features. However, existing models focus solely on pottery shape comparison, neglecting the analysis of their evolution across different historical periods. In this work, we propose a method based on deep learning to assist experts in identifying the evolutionary patterns of a given pottery type within their specified chronological divisions. First we train a convolutional neural network for pottery classification, extracting low and high level features that represent different ages of pottery samples. Next, we employ clustering algorithms to identify representative potteries for each historical period based on high level features. To facilitate intuitive comparisons across different ages, we use shallow features and compute cosine similarities between potteries, visualizing shape and decoration differences. This approach enhances understanding of pottery evolution patterns directly through visual analysis. The effectiveness and efficiency of our proposed method are evaluated by validating it on three distinct era division cases using data from the Dabagou and Miaozigou archaeological sites, which represent the Miaozigou culture and exhibit clear evolutionary patterns. Our method identifies representative artifacts for each era and uncovers their evolutionary patterns effectively and efficiently, achieving conclusions comparable to those of experts while significantly reducing time compared to traditional manual methods.