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Applications of Computer Vision, IEEE Workshop on (1998)
Princeton, New Jersey
Oct. 19, 1998 to Oct. 21, 1998
ISBN: 0-8186-8606-5
pp: 194
Marcus A Maloof , Georgetown University
Pat Langley , Institute for the Study of Learning and Expertise
Thomas O Binford , Stanford University
Ramakant Nevatia , University of Southern California
ABSTRACT
We present the results of an empirical study in which we evaluated cost-sensitive learning algorithms on a rooftop detection task, which is one level of processing in a building detection system. Specifically, we investigated how well machine learning methods generalized to unseen images that differed in location and in aspect. For the purpose of comparison, we included in our evaluation a handcrafted linear classifier, which is the selection heuristic currently used in the building detection system. ROC analysis showed that, when generalizing to unseen images that differed in location and aspect, a naive Bayesian classifier outperformed nearest neighbor and the handcrafted solution.
INDEX TERMS
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CITATION

R. Nevatia, T. O. Binford, P. Langley and M. A. Maloof, "Generalizing over Aspect and Location for Rooftop Detection," Applications of Computer Vision, IEEE Workshop on(WACV), Princeton, New Jersey, 1998, pp. 194.
doi:10.1109/ACV.1998.732879
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