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Exploiting Term Co-occurrence for Enhancing Automated Image Annotation

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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.

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Authors and Affiliations

  1. Knowledge Media Institute, The Open University, Walton Hall, Milton Keynes, MK7 6AA, United Kingdom

    Ainhoa Llorente, Haiming Liu, Rui Hu, Adam Rae & Stefan Rüger

  2. INFOTECH Unit, ROBOTIKER-TECNALIA, Parque Tecnológico, Edificio 202, Zamudio, E-48170, Bizkaia, Spain

    Ainhoa Llorente

  3. Department of Computing, Imperial College London, London, SW7 2AZ, United Kingdom

    Simon Overell

  4. University College London, Adastral Campus, Ipswich, Suffolk, IP5 3RE, United Kingdom

    Jianhan Zhu

  5. School of Computing, The Robert Gordon University, Andrew Street, Aberdeen, AB25 1HG, United Kingdom

    Dawei Song

Authors
  1. Ainhoa Llorente

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  2. Simon Overell

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  3. Haiming Liu

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  4. Rui Hu

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  5. Adam Rae

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  6. Jianhan Zhu

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  7. Dawei Song

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  8. Stefan Rüger

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Editor information

Editors and Affiliations

  1. Istituto di Scienza e Tecnologie dell’Informazione, CNR, Pisa, Italy

    Carol Peters

  2. RWTH Aachen University, Aachen, Germany

    Thomas Deselaers

  3. University of Padua, Padua, Italy

    Nicola Ferro

  4. LSI-UNED, Madrid, Spain

    Julio Gonzalo  & Anselmo Peñas  & 

  5. Dublin City University, Dublin 9, Ireland

    Gareth J. F. Jones

  6. Helsinki University of Technology, Espoo, Finland

    Mikko Kurimo

  7. University of Hildesheim, Hildesheim, Germany

    Thomas Mandl

  8. Humboldt University Berlin, Germany

    Vivien Petras

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© 2009 Springer-Verlag Berlin Heidelberg

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Llorente, A.et al. (2009). Exploiting Term Co-occurrence for Enhancing Automated Image Annotation. In: Peters, C.,et al. Evaluating Systems for Multilingual and Multimodal Information Access. CLEF 2008. Lecture Notes in Computer Science, vol 5706. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04447-2_79

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