Re-ranking for Writer Identification and Writer Retrieval

• URL: http://arxiv.org/abs/2007.07101v1
• Date: Tue, 14 Jul 2020 15:21:17 GMT
• Title: Re-ranking for Writer Identification and Writer Retrieval
• Authors: Simon Jordan, Mathias Seuret, Pavel Kr\'al, Ladislav Lenc, Ji\v{r}\'i Mart\'inek, Barbara Wiermann, Tobias Schwinger, Andreas Maier, Vincent Christlein
• Abstract summary: We show that a re-ranking step based on k-reciprocal nearest neighbor relationships is advantageous for writer identification. We use these reciprocal relationships in two ways: encode them into new vectors, as originally proposed, or integrate them in terms of query-expansion.
• Score: 8.53463698903858
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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