Exploiting time in automatic image tagging

  • Authors:
  • Philip J. McParlane;Joemon M. Jose

  • Affiliations:
  • Department of Computing Science, The University of Glasgow, Glasgow, UK;Department of Computing Science, The University of Glasgow, Glasgow, UK

  • Venue:
  • ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
  • Year:
  • 2013

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Abstract

Existing automatic image annotation (AIA) models that depend solely on low-level image features often produce poor results, particularly when annotating real-life collections. Tag co-occurrence has been shown to improve image annotation by identifying additional keywords associated with user-provided keywords. However, existing approaches have treated tag co-occurrence as a static measure over time, thereby ignoring the temporal trends of many tags. The temporal distribution of tags, however, caused by events, seasons, memes, etc. provide a strong source of evidence beyond keywords for AIA. In this paper we propose a temporal tag co-occurrence approach to improve upon the current state-of-the-art automatic image annotation model. By replacing the annotated tags with more temporally significant tags, we achieve statistically significant increases to annotation accuracy on a real-life timestamped image collection from Flickr.