{
  "id": "2007.07101",
  "version": "v1",
  "published": "2020-07-14T15:21:17.000Z",
  "updated": "2020-07-14T15:21:17.000Z",
  "title": "Re-ranking for Writer Identification and Writer Retrieval",
  "authors": [
    "Simon Jordan",
    "Mathias Seuret",
    "Pavel Král",
    "Ladislav Lenc",
    "Jiří Martínek",
    "Barbara Wiermann",
    "Tobias Schwinger",
    "Andreas Maier",
    "Vincent Christlein"
  ],
  "categories": [
    "cs.CV"
  ],
  "abstract": "Automatic writer identification is a common problem in document analysis. State-of-the-art methods typically focus on the feature extraction step with traditional or deep-learning-based techniques. In retrieval problems, re-ranking is a commonly used technique to improve the results. Re-ranking refines an initial ranking result by using the knowledge contained in the ranked result, e. g., by exploiting nearest neighbor relations. To the best of our knowledge, re-ranking has not been used for writer identification/retrieval. A possible reason might be that publicly available benchmark datasets contain only few samples per writer which makes a re-ranking less promising. We show that a re-ranking step based on k-reciprocal nearest neighbor relationships is advantageous for writer identification, even if only a few samples per writer are available. We use these reciprocal relationships in two ways: encode them into new vectors, as originally proposed, or integrate them in terms of query-expansion. We show that both techniques outperform the baseline results in terms of mAP on three writer identification datasets.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2020-07-14T15:21:17.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "writer retrieval",
      "re-ranking",
      "k-reciprocal nearest neighbor relationships",
      "feature extraction step",
      "benchmark datasets contain"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}