Jordan S, Seuret M, Král P, Lenc L, Martínek J, Wiermann B, Schwinger T, Maier A, Christlein V (2020)
Publication Type: Book chapter / Article in edited volumes
Publication year: 2020
Edited Volumes: Document Analysis Systems
Pages Range: 572-586
ISBN: 9783030570576
URI: https://arxiv.org/abs/2007.07101
DOI: 10.1007/978-3-030-57058-3_40
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.
APA:
Jordan, S., Seuret, M., Král, P., Lenc, L., Martínek, J., Wiermann, B.,... Christlein, V. (2020). Re-Ranking for Writer Identification and Writer Retrieval. In Document Analysis Systems. (pp. 572-586).
MLA:
Jordan, Simon, et al. "Re-Ranking for Writer Identification and Writer Retrieval." Document Analysis Systems. 2020. 572-586.
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