Machine Learning for Image Analysis
Machine vision researchers have developed many image understanding
systems, but their software often relies on heuristic knowledge that
transfers poorly to new images, much less to new domains, and there
remains considerable room for improving their behavior. One path to
such improvement uses machine learning to refine or replace the
handcrafted knowledge that currently guides an image understanding
This project aims to embed learning algorithms into an existing
system that analyzes satellite images of military sites. The enhanced
system incorporates an visual interface that lets the developer
interactively label candidates about objects in the image, which it
then converts to training data for use in learning. An important
feature of the induction process is that it takes into acount the
relative costs for different types of errors. Preliminary tests show
the addition of machine learning improves the accuracy of building
recognition over that obtained with handcrafted knowledge. Future work
will include applying this approach to the recognition of lower-level
components of buildings, such as U contours and junctions.
This work was funded by the Office of Naval Research through Grant
Contributors to the Project
Dr. Kamal Ali
Professor Thomas Binford
Professor Pat Langley
Professor Marcus Maloof
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., Sage, S., & Binford, T. (1997).
Learning to detect rooftops in aerial images.
Proceedings of the 1997 Image Understanding Workshop.
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.
For more information, please send email to