Re-Ranking for Writer Identification and Writer Retrieval
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F20%3A43959619" target="_blank" >RIV/49777513:23520/20:43959619 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/chapter/10.1007/978-3-030-57058-3_40" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-030-57058-3_40</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-030-57058-3_40" target="_blank" >10.1007/978-3-030-57058-3_40</a>
Alternative languages
Result language
angličtina
Original language name
Re-Ranking for Writer Identification and Writer Retrieval
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
O - Projekt operacniho programu
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
14th IAPR International Workshop, DAS 2020, Wuhan, China, July 26-29,2020 proceedings
ISBN
978-3-030-57057-6
ISSN
0302-9743
e-ISSN
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Number of pages
14
Pages from-to
572-586
Publisher name
Springer
Place of publication
Cham
Event location
Wuhan, Čína
Event date
Jul 26, 2020
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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