On the application of the Wasserstein metric to 2D curves classification
Computer Vision and Pattern Recognition
2026-01-13 v1
Abstract
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.
Keywords
Cite
@article{arxiv.2601.07749,
title = {On the application of the Wasserstein metric to 2D curves classification},
author = {Agnieszka Kaliszewska and Monika Syga},
journal= {arXiv preprint arXiv:2601.07749},
year = {2026}
}