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Müller, Eric [Author]; Springstein, Matthias [Author]; Ewerth, Ralph [Author]; Jose, Joemon M. [Author]; Hauff, Claudia [Author]; Altıngövde, Ismail Sengor [Author]; Song, Dawei [Author]; Albakour, Dyaa [Author]; Watt, Stuart [Author]; Tait, John [Author]

“When Was This Picture Taken?” – Image Date Estimation in the Wild - [published Version]

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  • Media type: E-Article; Text
  • Title: “When Was This Picture Taken?” – Image Date Estimation in the Wild
  • Contributor: Müller, Eric [Author]; Springstein, Matthias [Author]; Ewerth, Ralph [Author]; Jose, Joemon M. [Author]; Hauff, Claudia [Author]; Altıngövde, Ismail Sengor [Author]; Song, Dawei [Author]; Albakour, Dyaa [Author]; Watt, Stuart [Author]; Tait, John [Author]
  • imprint: Berlin; Heidelberg : Springer, 2017
  • Published in: Advances in information retrieval : 39th European Conference on IR Research, ECIR 2017 ; Lecture Notes in Computer Science (LNCS) ; 1019
  • Issue: published Version
  • Language: English
  • DOI: https://doi.org/10.15488/16288; https://doi.org/10.1007/978-3-319-56608-5_57
  • ISBN: 978-3-319-56607-8; 978-3-319-56608-5
  • ISSN: 0302-9743
  • Keywords: Neural networks ; Image dates ; Convolutional neural network ; Deep neural networks ; Konferenzschrift ; Information retrieval
  • Origination:
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  • Description: The problem of automatically estimating the creation date of photos has been addressed rarely in the past. In this paper, we introduce a novel dataset Date Estimation in the Wild for the task of predicting the acquisition year of images captured in the period from 1930 to 1999. In contrast to previous work, the dataset is neither restricted to color photography nor to specific visual concepts. The dataset consists of more than one million images crawled from Flickr and contains a large number of different motives. In addition, we propose two baseline approaches for regression and classification, respectively, relying on state-of-the-art deep convolutional neural networks. Experimental results demonstrate that these baselines are already superior to annotations of untrained humans.
  • Access State: Open Access
  • Rights information: Attribution (CC BY)