{
  "annotation_id": "3422a81c-7b89-4fd4-9758-f061ca4e0f29",
  "date_created": "2026-02-17T05:53:12.448000Z",
  "date_modified": "2026-02-17T05:53:12.448000Z",
  "file_hash": "b59cd69c50464c58045fb5144766332b1d0a1a20a90193a6129b1c9d72b93b50",
  "private": false,
  "record": {
    "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.",
    "arxiv_id": "2601.07749",
    "authors": [
      "Agnieszka Kaliszewska",
      "Monika Syga"
    ],
    "categories": [
      "cs.CV"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "title": "On the application of the Wasserstein metric to 2D curves classification",
    "url": "https://arxiv.org/abs/2601.07749",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "bc646c06-ba7b-4646-905b-eca5f4f442aa",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}