论文标题
通过变异自动编码器对罗马陶器的无监督聚类
Unsupervised Clustering of Roman Potsherds via Variational Autoencoders
论文作者
论文摘要
在本文中，我们提出了一种人工智能成像解决方案，以支持罗马通用软件的分类任务中的考古学家。通常，每个陶器都以其截面形式为二维黑白图像，并用与特定考古发掘有关的考古书籍印刷。这些碎片的局部性和手工差异使它们的匹配问题是一个具有挑战性的问题：我们建议通过在深度卷积变量自动编码器（VAE）网络的潜在空间中学习的非线性特征的无监督分层聚类对相似的概况进行配对。我们的贡献还包括创建罗马通用软件陶器（Rocopot）数据库，其中4000多个Potsherds配置文件从25个Roman Pottery Corpera中提取，以及一个轻松检查形状相似性的Matlab Gui软件。从数学和考古学的角度来评论结果，以解锁两个社区的新研究方向。
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.
