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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