Date Estimation in the Wild of Scanned Historical Photos: An Image Retrieval Approach

Adrià Molina*, Pau Riba, Lluis Gomez, Oriol Ramos-Terrades, Josep Lladós

*Corresponding author for this work

Research output: Book/ReportProceedingResearchpeer-review

2 Citations (Scopus)


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.

Original languageEnglish
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages15
ISBN (Print)9783030863302
Publication statusPublished - 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12822 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


  • Date estimation
  • Historical photographs
  • Image retrieval
  • Ranking loss
  • Smooth-nDCG


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