Machine Learning for Image Analysis
This project focused on applying machine learning techniques to
problems in machine vision. Our early efforts focused on adapting
learning methods to acquire three-dimensional models of object classes
from training images. In more recent work, we evaluated a variety of
induction techniques (e.g., CN2, C4.5, nearest neighbor, naive Bayes,
perceptron learning) on their ability to aid detection of rooftops in
aerial photographs. Our training and test data were provided by Ram
Nevatia and his colleagues in the
Institute for Robotics and Intelligent Systems at the
University of Southern California.
The detection of rooftops is one of many levels of processing in the
USC-developed BUDDS vision system. At each level of processing, it
vision system posits a set of hypotheses (e.g., roof hypotheses), which
are accepted (i.e., roofs) or rejected (i.e., non-roofs) by a selection
process. The implementation we were attempting to improve upon used a
hand-configured linear classifier to select and reject roof hypotheses.
Our research hypothesis was that machine learning algorithms could provide
a faster method for configuring classifiers for this task and that the
accuracy of the machine-learned classifiers will be equivalent to,
if not better than, the accuracy of the hand-coded classifiers.
This work was funded by the Image Understanding Program of the Defense
Advanced Research Projects Agency, through Grant N00014-94-1-0746 from
the Office of Naval Research, and
through an equiment grant from
One of the aerial photographs used in our experiments on learning to
Maloof, M. A., Langley, P., Binford, T. O., Nevatia, R., & Sage, S. (2003).
Improved rooftop detection in aerial images with machine learning.
Machine Learning, 53, 157-191.
Ali, K. M., Langley, P., Maloof, M. A., Binford, T. O., & Sage, S.
Improving rooftop detection with interactive visual learning.
Proceedings of the Image Understanding Workshop.
Monterrey, CA: Morgan Kaufmann.
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. Princeton, NJ: IEEE Press.
Maloof, M. A., Langley, P., Binford, T., & Sage, S. (1998).
Improving rooftop detection in aerial images through machine learning
(Technical Report 98-1). Institute for the Study of Learning and
Expertise, Palo Alto, CA.
Maloof, M. A., Langley, P., Sage, S., & Binford, T. (1997).
Learning to detect rooftops in aerial images.
Proceedings of the 1997 Image Understanding Workshop (pp. 835-845).
New Orleans: Morgan Kaufmann.
Provan, G., Langley, P., & Binford, T.O. (1996).
Probabilistic learning of three-dimensional object models.
Proceedings of the Image Understanding Workshop (pp. 1403-1413).
Palm Springs, CA: Morgan Kaufmann.
Langley, P., Binford, T. O., & Levitt, T. S. (1994).
Learning object models from visual observation and background
knowledge. Proceedings of the Image Understanding Workshop.
Bowyer, K. W., Hall, L. O., Langley, P., Bhanu, B., & Draper, B. A.
Report of the AAAI fall symposium on machine learning and computer
vision: What, why and how.
Proceedings of the Image Understanding Workshop. Monterrey, CA.
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