Unsupervised Clustering of Roman Potsherds via Variational Autoencoders

• URL: http://arxiv.org/abs/2203.07437v1
• Date: Mon, 14 Mar 2022 18:56:13 GMT
• Title: Unsupervised Clustering of Roman Potsherds via Variational Autoencoders
• Authors: Simone Parisotto, Ninetta Leone, Carola-Bibiane Sch\"onlieb, Alessandro Launaro
• Abstract summary: We propose an artificial intelligence solution to support archaeologists in the classification task of Roman commonware potsherds. The partiality and handcrafted variance of the fragments make their matching a challenging problem. We propose to pair similar profiles via the unsupervised hierarchical clustering of non-linear features learned in the latent space of a deep convolutional Variational Autoencoder (VAE) network.
• Score: 63.8376359764052
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper we propose an artificial intelligence imaging solution to support archaeologists in the classification task of Roman commonware potsherds. Usually, each potsherd is represented by its sectional profile as a two dimensional black-white image and printed in archaeological books related to specific archaeological excavations. The partiality and handcrafted variance of the fragments make their matching a challenging problem: we propose to pair similar profiles via the unsupervised hierarchical clustering of non-linear features learned in the latent space of a deep convolutional Variational Autoencoder (VAE) network. Our contribution also include the creation of a ROman COmmonware POTtery (ROCOPOT) database, with more than 4000 potsherds profiles extracted from 25 Roman pottery corpora, and a MATLAB GUI software for the easy inspection of shape similarities. Results are commented both from a mathematical and archaeological perspective so as to unlock new research directions in both communities.

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