Knowledge Reuse Mechanisms for Categorizing Related Image Sets

This chapter introduces the concept of {\em classifier knowledge
reuse} as a means of exploiting domain knowledge taken from old,
previously created, relevant classifiers to assist in a new
classification task.  Knowledge reuse helps in constructing better
generalizing classifiers given few training examples and for
evaluating images for search in an image database.  In particular, we
discuss a knowledge reuse framework in which a {\em supra-classifier}
improves the performance of the {\em target classifier} using
information from existing {\em support classifiers}. Soft computing
methods can be used for all three types of classifiers involved.  We
explore supra-classifier design issues and introduce several types of
supra-classifiers, comparing their relative strengths and weaknesses.
Empirical examples on real world image data sets are used to
demonstrate the effectiveness of the supra-classifier framework for
classification and retrieval/search in image databases.