Generalizing over aspect and location for rooftop detection

Marcus A. Maloof, Pat Langley, Thomas O. Binford, and Ram Nevatia

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.

Paper available in PostScript (gzipped) and PDF.

Slides from the talk available in PostScript (gzipped) and PDF.

Maloof, M.A., Langley, P., Binford, T.O., & Nevatia, R. (1998). Generalizing over aspect and location for rooftop detection. Proceedings of the Fourth IEEE Workshop on Applications of Computer Vision (WACV '98), 194–199.

@inproceedings{maloof.wacv.98,
  author = "Maloof, M.A. and Langley, P. and Binford, T. and Nevatia, R.",
  title = "Generalizing over aspect and location for rooftop detection",
  booktitle = "{Proceedings of the Fourth IEEE Workshop on Applications of
    Computer Vision (WACV '98)}",
  year = 1998,
  pages = "194--199",
  publisher = "IEEE Press",
  address = "Los Alamitos, CA"
}