Classification of Ceramic Shards Based on Convolutional Neural Network
Aladine Chetouani, Teddy Debroutelle, Sylvie Treuillet, Matthieu Exbrayat, Sébastien Jesset · 2018
ARCADIA project aims to enhance the archaeological heritage of ceramic shards extracted in Saran (France). Dating from the High Middle Ages, these shards have been engraved by repeated patterns using a carved wooden wheel. The study of these shards allows the archeologists to better understand the diffusion of ceramic productions. In this paper, we propose to exploit Convolutional Neural Network (CNN) models to classify automatically these ceramic shards. The ultimate goal is to form clusters of shards to derive a map that represents the movements of potters. For that, several models have been tested and compared to some well-known handcrafted-methods. The Fully Connected part of the best model was modified to see its impact in terms of classification. A dataset composed of 888 binary images of ceramic shards was used. The results obtained outperform the state-of-the-art methods and show the relevance of the proposed approach.