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Digital Image Similarity for Geo-spatial Knowledge Management

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Advances in Case-Based Reasoning (ECCBR 2002)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2416))

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Abstract

The amount and availability of high-quality geo-spatial image data, such as digital satellite and aerial photographs, is increasing dramatically. Task-based management of such visual information and associated knowledge is a central concern for organisations that rely on digital imagery. We are developing geo-spatial knowledge management techniques that employ case-based reasoning as the core methodology. In order to provide effective retrieval of task-based experiences that center around geo-spatial imagery, we need to forward novel similarity metrics for directly comparing the image components of experience cases. Based on work in geo-spatial image database retrieval, we are building an effective similarity metric for geo-spatial imagery that makes comparisons based on derived image features, their shapes, and the spatial relations between them. This paper gives an overview of the geo-spatial knowledge management context, describes our image similarity metric, and provides an initial evaluation of the work.

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Author information

Authors and Affiliations

  1. Digital Media Centre, Dublin Institute of Technology, Dublin 2, Ireland

    James D. Carswell

  2. Smart Media Institute, Department of Computer Science, University College Dublin, Belfield, Dublin 4, Ireland

    David C. Wilson & Michela Bertolotto

Authors
  1. James D. Carswell
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  2. David C. Wilson
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  3. Michela Bertolotto
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Editor information

Editors and Affiliations

  1. School of Computing, The Robert Gordon University, St. Andrew Street, Aberdeen, AB25 1HG, Scotland, UK

    Susan Craw

  2. Computing Science Department, University of Aberdeen, Aberdeen, AB24 3UE, Scotland, UK

    Alun Preece

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© 2002 Springer-Verlag Berlin Heidelberg

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Carswell, J.D., Wilson, D.C., Bertolotto, M. (2002). Digital Image Similarity for Geo-spatial Knowledge Management. In: Craw, S., Preece, A. (eds) Advances in Case-Based Reasoning. ECCBR 2002. Lecture Notes in Computer Science(), vol 2416. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46119-1_6

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  • DOI: https://doi.org/10.1007/3-540-46119-1_6

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-44109-0

  • Online ISBN: 978-3-540-46119-7

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