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We introduce a method for rapidly classifying visual scenes globally along a small number of navigationally relevant dimensions: depth of scene, presence of obstacles, path versus nonpath, and orientation of path. We show that the algorithm reliably classifies scenes in terms of these high-level features, based on global or coarsely localized spectral analysis analogous to early-stage biological vision. We use this analysis to implement a real-time visual navigational system on a mobile robot, trained online by a human operator. We demonstrate successful training and subsequent autonomous path following for two different outdoor environments, a running track and a concrete trail. Our success with this technique suggests a general applicability to autonomous robot navigation in a variety of environments.