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Unsupervised clustering of Roman pottery profiles from their SSAE representation

2020-06-04 · Simone Parisotto, Alessandro Launaro, Ninetta Leone, Carola-Bibiane Schönlieb

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.

📄 PDF Abstract BibTeX arXiv:2006.03156

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Clustering

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Sparse Autoencoder A Sparse Autoencoder is a type of autoencoder that employs sparsity to achieve an information bottleneck. Specifically the loss function is constructed so that activations are…
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