Knowledge Reuse Mechanisms of Categorizing Related Image Sets

摘要

This chapter introduces the concept of 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 supra-classifier improves the performance of the target classifier using information from existing 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.