8th International Conference on
Computational Harmonic Analysis

September 12-16, 2022

Ingolstadt, Germany

"An application of harmonic analysis to the digital humanities"

Heinecke, Andreas

Die analysis is an important tool of ancient economic history. Yet, manual die studies are too labor-intensive to comprehensively study large coinages such as those of the Roman Empire. From a computer vision viewpoint, such studies present a challenging unsupervised clustering problem, involving an unknown and large number of highly similar semantic classes (usually hundreds) of imbalanced sizes, for which training examples would be extremely time-expensive to obtain and are thus not available. We address this problem through determining dissimilarities derived from specifically devised re-weighted Gaussian process-based keypoint features in a Bayesian distance microclustering clustering framework. The covariance structure of the re-weighted Gaussian process can be efficiently approximated via convolutions. The resulting method can reduce the time investment necessary for large-scale die studies by several orders of magnitude, in many cases from years to weeks, and provide data towards longstanding controversial debates on the economic history of ancient states.

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