On the application of the Wasserstein metric to 2D curves classification
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| _version_ | 1866918284154109952 |
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| author | Kaliszewska, Agnieszka Syga, Monika |
| author_facet | Kaliszewska, Agnieszka Syga, Monika |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_07749 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | On the application of the Wasserstein metric to 2D curves classification Kaliszewska, Agnieszka Syga, Monika Computer Vision and Pattern Recognition 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. |
| title | On the application of the Wasserstein metric to 2D curves classification |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2601.07749 |