Exploiting term co-occurrence for enhancing automated image annotation

  • Authors:

  • Ainhoa Llorente;Simon Overell;Haiming Liu;Rui Hu;Adam Rae;Jianhan Zhu;Dawei Song;Stefan Rüger

  • Affiliations:

  • Knowledge Media Institute, The Open University, Milton Keynes, United Kingdom and INFOTECH Unit, ROBOTIKER-TECNALIA, Parque Tecnológico, Bizkaia, Spain;Department of Computing, Imperial College London, London, United Kingdom;Knowledge Media Institute, The Open University, Milton Keynes, United Kingdom;Knowledge Media Institute, The Open University, Milton Keynes, United Kingdom;Knowledge Media Institute, The Open University, Milton Keynes, United Kingdom;University College London, Suffolk, United Kingdom;School of Computing, The Robert Gordon University, Aberdeen, United Kingdom;Knowledge Media Institute, The Open University, Milton Keynes, United Kingdom

  • Venue:
  • CLEF'08 Proceedings of the 9th Cross-language evaluation forum conference on Evaluating systems for multilingual and multimodal information access
  • Year:
  • 2008

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Abstract

This paper describes an application of statistical co-occurrence techniques that built on top of a probabilistic image annotation framework is able to increase the precision of an image annotation system. We observe that probabilistic image analysis by itself is not enough to describe the rich semantics of an image. Our hypothesis is that more accurate annotations can be produced by introducing additional knowledge in the form of statistical co-occurrence of terms. This is provided by the context of images that otherwise independent keyword generation would miss. We applied our algorithm to the dataset provided by ImageCLEF 2008 for the Visual Concept Detection Task (VCDT). Our algorithm not only obtained better results but also it appeared in the top quartile of all methods submitted in ImageCLEF 2008.