Details
| Original language | English |
|---|---|
| Title of host publication | Advances in Information Retrieval |
| Subtitle of host publication | 39th European Conference on IR Research, ECIR 2017, Proceedings |
| Editors | Claudia Hauff, Joemon M. Jose, Dyaa Albakour, Ismail Sengor Altingovde, John Tait, Dawei Song, Stuart Watt |
| Publisher | Springer Verlag |
| Pages | 619-625 |
| Number of pages | 7 |
| ISBN (print) | 9783319566078 |
| Publication status | Published - 8 Apr 2017 |
| Event | 39th European Conference on Information Retrieval, ECIR 2017 - Aberdeen, United Kingdom (UK) Duration: 8 Apr 2017 → 13 Apr 2017 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 10193 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (electronic) | 1611-3349 |
Abstract
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.
ASJC Scopus subject areas
- Mathematics(all)
- Theoretical Computer Science
- Computer Science(all)
- General Computer Science
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Advances in Information Retrieval: 39th European Conference on IR Research, ECIR 2017, Proceedings. ed. / Claudia Hauff; Joemon M. Jose; Dyaa Albakour; Ismail Sengor Altingovde; John Tait; Dawei Song; Stuart Watt. Springer Verlag, 2017. p. 619-625 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10193 LNCS).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - “When was this picture taken?”
T2 - 39th European Conference on Information Retrieval, ECIR 2017
AU - Springstein, Matthias
AU - Ewerth, Ralph
AU - Müller-Budack, Eric
N1 - Publisher Copyright: © The Author(s) 2017. Copyright: Copyright 2017 Elsevier B.V., All rights reserved.
PY - 2017/4/8
Y1 - 2017/4/8
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85018711237&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-56608-5_57
DO - 10.1007/978-3-319-56608-5_57
M3 - Conference contribution
AN - SCOPUS:85018711237
SN - 9783319566078
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 619
EP - 625
BT - Advances in Information Retrieval
A2 - Hauff, Claudia
A2 - Jose, Joemon M.
A2 - Albakour, Dyaa
A2 - Altingovde, Ismail Sengor
A2 - Tait, John
A2 - Song, Dawei
A2 - Watt, Stuart
PB - Springer Verlag
Y2 - 8 April 2017 through 13 April 2017
ER -