Re-Ranking for Writer Identification and Writer Retrieval
14th IAPR International Workshop on Document Analysis Systems (2020)
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.
Authors
BibTex
@misc{https://doi.org/10.48550/arxiv.2007.07101, doi = {10.48550/ARXIV.2007.07101}, url = {https://arxiv.org/abs/2007.07101}, author = {Jordan, Simon and Seuret, Mathias and Král, Pavel and Lenc, Ladislav and Martínek, Jiří and Wiermann, Barbara and Schwinger, Tobias and Maier, Andreas and Christlein, Vincent}, keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Re-ranking for Writer Identification and Writer Retrieval}, publisher = {arXiv}, year = {2020}, copyright = {arXiv.org perpetual, non-exclusive license} }
