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Why Case-Based Reasoning is Attractive for Image Interpretation

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Case-Based Reasoning Research and Development (ICCBR 2001)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2080))

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Abstract

The development of image interpretation systems is concerned with tricky problems such as a limited number of observations, environmental influence, and noise. Recent systems lack robustness, accuracy, and flexibility. The introduction of case-based reasoning (CBR) strategies can help to overcome these drawbacks. The special type of information (i.e., images) and the problems mentioned above provide special requirements for CBR strategies. In this paper we review what has been achieved so far and research topics concerned with case-based image interpretation. We introduce a new approach for an image interpretation system and review its components.

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Author information

Authors and Affiliations

  1. Institute of Computer Vision and Applied Computer Sciences, Arno-Nitzsche-Str. 45, 04277, Leipzig, Germany

    Petra Perner

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  1. Petra Perner
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Editor information

Editors and Affiliations

  1. Naval Research Laboratory, Code 5510, Navy Center for Applied Research in Artificial Intelligence, 4555 OverlookAvenue, SW, Washington, DC, 20375, USA

    David W. Aha

  2. Computer Science Department, University of Auckland, Private Bag 92019, Auckland 1, New Zealand

    Ian Watson

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© 2001 Springer-Verlag Berlin Heidelberg

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Perner, P. (2001). Why Case-Based Reasoning is Attractive for Image Interpretation. In: Aha, D.W., Watson, I. (eds) Case-Based Reasoning Research and Development. ICCBR 2001. Lecture Notes in Computer Science(), vol 2080. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44593-5_3

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  • DOI: https://doi.org/10.1007/3-540-44593-5_3

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42358-4

  • Online ISBN: 978-3-540-44593-7

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