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Introduction to Case-Based Reasoning for Signals and Images

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Book cover Case-Based Reasoning on Images and Signals

Part of the book series: Studies in Computational Intelligence ((SCI,volume 73))

Summary

Case-based reasoning (CBR) is used when generalized knowledge is lacking. The method works on a set of cases formerly processed and stored in the case base. A new case is interpreted based on its similarity to cases in the case base. The closest case with its associated result is selected and presented as output of the system. Signal-interpreting systems for 1-d, 2-d, or 3-dimensional signals are becoming increasingly popular in medical and industrial applications. New strategies are necessary that can adapt to changing environmental conditions, user needs, and process requirements. Introducing CBR strategies into signal-interpreting systems can satisfy these requirements. We describe in this chapter the basics of CBR and review what has been done so far in the field of signal-interpreting systems.

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  1. Institute of Computer Vision and Applied Computer Sciences Leipzig, Arno-Nitzsche-Str. 43, 04277, Leipzig, Germany

    P. Perner

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  1. Institute of Computer Vision and Applied Computer Sciences, IBaI, Arno-Nitzsche-Str.43, 04277, Leipzig, Germany

    Petra Perner

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Perner, P. (2008). Introduction to Case-Based Reasoning for Signals and Images. In: Perner, P. (eds) Case-Based Reasoning on Images and Signals. Studies in Computational Intelligence, vol 73. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73180-1_1

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  • DOI: https://doi.org/10.1007/978-3-540-73180-1_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-73178-8

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