Building pixel classifiers using the interactive teacher/learner (ITL) system
We present an interactive paradigm for the construction of pixel classifiers. The user selects training pixels incrementally, based on real-time feedback from the classifier running in the background. Experiments show that this facilitates the construction of very small yet accurate decision tree classifiers. The framework is extensible in many ways. For example, we show how classifiers can be composed hierarchically and trained to find seagulls in aerial images.