Re-ranking for Writer Identification and Writer Retrieval

Simon Jordan; Mathias Seuret; Pavel Král; Ladislav Lenc; Jiří Martínek; Barbara Wiermann; Tobias Schwinger; Andreas Maier; Vincent Christlein

arXiv · 2007.07101

Re-ranking for Writer Identification and Writer Retrieval

Simon Jordan·Mathias Seuret·Pavel Král·Ladislav Lenc·Jiří Martínek·Barbara Wiermann·Tobias Schwinger·Andreas Maier·Vincent Christlein

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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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Simon Jordan, Mathias Seuret, Pavel Král, Ladislav Lenc, Jiří Martínek, Barbara Wiermann, Tobias Schwinger, Andreas Maier, Vincent Christlein. 2020-07-14. Re-ranking for Writer Identification and Writer Retrieval. https://arxiv.org/abs/2007.07101

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