From classification to matching: A CNN-based approach for retrieving painted pottery images

Recently artificial intelligence has begun to assist archaeologists in processing images of archaeological artifacts. We report a convolutional neural network approach to obtain feature vectors of painted pottery images by a preliminary classification machine learning of the cultural types. The model, trained on a photographic image dataset of Chinese Neolithic color-painted pottery, achieved 92.58% precision in assigning vessel images to corresponding archaeological types. The feature vectors contain information of vessel shape, color, and ornamentation design, based on which similarity coefficients for the images in the dataset were calculated. The quantitative measurement of similarity allows searching for the closest match to artefacts in the dataset, as well as a network of vessels in terms of similarity. This work highlights the potential of CNN approaches in curating of archaeological artifacts, providing a new tool assisting to study chronology, typology, decoration design, etc.

近年来，人工智能技术已开始辅助考古学家处理考古文物的图像。本研究报道了一种基于卷积神经网络的方法，通过初步的文化类型机器学习分类，获得彩陶图像的特征向量。该模型在对中国新石器时代彩陶的摄影图像数据集上进行训练，实现了对器物图像与其相应考古类型的92.58%精确度。特征向量包含了器物形状、颜色以及装饰设计的信息，据此计算了数据集中图像的相似系数。相似度的定量测量使得对数据集中文物的最接近匹配搜索成为可能，同时也形成了一个基于相似度的器物网络。本研究突出了卷积神经网络在考古文物整理中的潜力，为研究年代、类型学、装饰设计等领域提供了一种新的辅助工具。