Adaptive selection of image classifiers

  • Giorgio Giacinto
  • Fabio Roli
Session 2: Image Analysis & Pattern Recognition
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1310)


Recently, the concept of “Multiple Classifier Systems” was proposed as a new approach to the development of high performance image classification systems. Multiple Classifier Systems can be used to improve classification accuracy by combining the outputs of classifiers making “uncorrelated” errors. Unfortunately, in real image recognition problems, it may be very difficult to design an ensemble of classifiers that satisfies this assumption. In this paper, we propose a different approach based on the concept of “adaptive selection” of multiple classifiers in order to select the most appropriate classifier for each input pattern. We point out that adaptive selection does not require the assumption of uncorrelated errors, thus simplifying the choice of classifiers forming a Multiple Classifier System. Reported results on the classification of remote-sensing images show that adaptive selection can be used to obtain substantial improvements in classification accuracy.


Classification Accuracy Test Pattern Input Pattern Select Condition Adaptive Selection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Giorgio Giacinto
    • 1
  • Fabio Roli
    • 1
  1. 1.Dept. of Electrical and Electronic Eng.University of Cagliari, ItalyCagliariItaly

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