  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.

Computer Vision and Pattern Recognition