Date Estimation in the Wild of Scanned Historical Photos: An Image Retrieval Approach
This work addresses date estimation for archival historical photos, which is an incremental advancement in domain-specific image analysis.
The paper tackles the problem of estimating dates for historical photographs by framing it as an image retrieval task, using a learning objective based on the nDCG ranking metric, and reports improved performance over baseline methods on the DEW database.
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.