On the application of the Wasserstein metric to 2D curves classification
This work addresses classification challenges in archaeology by enabling targeted analysis of curve fragments, but it is incremental as it adapts existing methods.
The authors tackled the problem of classifying 2D curves by developing variants of the Wasserstein distance that focus on specific fragments, and they tested this approach through clustering experiments on archaeological data.
In this work we analyse a number of variants of the Wasserstein distance which allow to focus the classification on the prescribed parts (fragments) of classified 2D curves. These variants are based on the use of a number of discrete probability measures which reflect the importance of given fragments of curves. The performance of this approach is tested through a series of experiments related to the clustering analysis of 2D curves performed on data coming from the field of archaeology.