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.
Building pixel classifiers using the interactive teacher/learner (ITL) system
01.01.1998
381201 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Building Pixel Classifiers Using the Interactive Teacher/Learner (ITL) System
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