Unsupervised clustering of Roman pottery profiles from their SSAE representation

TL;DR

This paper presents an unsupervised clustering approach for Roman pottery profiles using a stacked sparse autoencoder to learn features, enabling the discovery of new profile matches from a large, variably shaped database.

Contribution

The introduction of the ROCOPOT database and the application of SSAE-based hierarchical clustering for pottery profile analysis are novel contributions.

Findings

Unveiled new pottery profile matches.

Demonstrated effectiveness of SSAE in feature learning.

Bridged mathematical and archaeological insights.

Abstract

In this paper we introduce the ROman COmmonware POTtery (ROCOPOT) database, which comprises of more than 2000 black and white imaging profiles of pottery shapes extracted from 11 Roman catalogues and related to different excavation sites. The partiality and the handcrafted variance of the shape fragments within this new database make their unsupervised clustering a very challenging problem: profile similarities are thus explored via the hierarchical clustering of non-linear features learned in the latent representation space of a stacked sparse autoencoder (SSAE) network, unveiling new profile matches. Results are commented both from a mathematical and archaeological perspective so as to unlock new research directions in the respective communities.