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 artifacts 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.