Abstract
This paper presents a novel method for date estimation of historical photographs from archival sources. The main contribution is to formulate the date estimation as a retrieval task, where given a query, the retrieved images are ranked in terms of the estimated date similarity. The closer are their embedded representations the closer are their dates. Contrary to the traditional models that design a neural network that learns a classifier or a regressor, we propose a learning objective based on the nDCG ranking metric. We have experimentally evaluated the performance of the method in two different tasks: date estimation and date-sensitive image retrieval, using the DEW public database, overcoming the baseline methods.
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Notes
- 1.
- 2.
Using public Python library ANNOY for Approximate Nearest Neighbours https://github.com/spotify/annoy.
- 3.
This is an extended version of Müller et al. [9] made publicly available by the same authors at https://github.com/TIB-Visual-Analytics/DEW-Model.
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Acknowledgment
This work has been partially supported by the Spanish projects RTI2018-095645-B-C21, and FCT-19-15244, and the Catalan projects 2017-SGR-1783, the Culture Department of the Generalitat de Catalunya, and the CERCA Program/Generalitat de Catalunya.
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Molina, A., Riba, P., Gomez, L., Ramos-Terrades, O., Lladós, J. (2021). Date Estimation in the Wild of Scanned Historical Photos: An Image Retrieval Approach. In: Lladós, J., Lopresti, D., Uchida, S. (eds) Document Analysis and Recognition – ICDAR 2021. ICDAR 2021. Lecture Notes in Computer Science(), vol 12822. Springer, Cham. https://doi.org/10.1007/978-3-030-86331-9_20
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