For typical classification tasks, all training data are prepared in advance and are supplied to the classifier all at once. This is unnecessarily expensive and incurs overfitting problems, since the individual contributions of the training instances to the classifier are not known. We address this by proposing an interactive incremental framework for image classifier construction, where small numbers of training examples are supplied at each user interaction. After incorporating new training instances, the classifier immediately reclassifies the image to provide the user with instant feedback. This allows the user to choose additional informative training pixels from among the currently misclassified ones. Using a realistic terrain classification task, we demonstrate the potential of our method to generate small and accurate decision tree classifiers from surprisingly few training examples while avoiding overspecialization. We also briefly discuss the novel concept of hierarchical clas..
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