Abstract
Dramatic improvements in sensor and image acquisition technology have created a demand for automated tools that can aid in the analysis of large image databases. We describe the development of JARtool, a trainable software system that learns to recognize volcanoes in a large data set of Venusian imagery. A machine learning approach is used because it is much easier for geologists to identify examples of volcanoes in the imagery than it is to specify domain knowledge as a set of pixel-level constraints. This approach can also provide portability to other domains without the need for explicit reprogramming; the user simply supplies the system with a new set of training examples. We show how the development of such a system requires a completely different set of skills than are required for applying machine learning to “toy world” domains. This paper discusses important aspects of the application process not commonly encountered in the “toy world,” including obtaining labeled training data, the difficulties of working with pixel data, and the automatic extraction of higher-level features.
Article PDF
Similar content being viewed by others
References
Asker, L. & Maclin, R. (1997a). Ensembles as a sequence of classifiers. Fifteenth International Joint Conference on Artificial Intelligence.
Asker, L. & Maclin, R. (1997b). Achieving expert performance on real-world problems using machine learning. Fourteenth International Conference on Machine Learning.
Aubele, J.C. & Slyuta, E. N. (1990). Small domes onVenus: characteristics and origins. Earth, Moon and Planets, 50/51:493-532.
Baldi, P. (1994). Personal communication.
Brodley, C.E. & Smyth, P. (1997). Applying classification algorithms in practice. Statistics and Computing.
Bunch, P.C., Hamilton, J.F., Sanderson, G.K. & Simmons, A.H. (1978). A free-response approach to the measurement and characterization of radiographic-observer performance. J. Appl. Photo. Eng., 4(4):166-171.
Burl, M.C., Fayyad, U.M., Perona, P., Smyth, P. & Burl, M.P. (1994a). Automating the hunt for volcanoes on Venus. Proceedings of the Computer Vision and Pattern Recognition Conference (pp. 302-309). Los Alamitos, CA: IEEE Computer Society Press.
Burl, M.C., Fayyad, U.M., Perona, P. & Smyth, P. (1994b). Automated analysis of radar imagery of Venus: handling lack of ground truth. IEEE International Conference on Image Processing, volume III, pp. 236-240.
Burl, M.C., Fayyad, U.M., Perona, P. & Smyth, P. (1996). Trainable cataloging for digital image libraries with applications to volcano detection. (Technical Report CNS-TR-96-01). Pasadena, CA: California Institute of Technology, Dept. of CNS.
Chakraborty, D.P. & Winter, L.H.L. (1990). Free-response methodology: alternate analysis and a new observer-performance experiment. Radiology, 174:873-881.
Cherkauer, K. (1996). Personal communication.
Chesters, M.S. (1992). Human visual perception and ROC methodology in medical imaging. Physics in Medicine and Biology 37(7):1433-1476.
Cooke, R.M. (1991). Experts in Uncertainty. New York,NY: Oxford University Press.
Cross, A.M. (1988). Detection of circular geological features using the Hough transform. International Journal of Remote Sensing, 9(9):1519-1528.
Crumpler, L.S., Aubele, J.C., Senske, D.A., Keddie, S.T., Magee, K.P. & Head, J.W. (1997). Volcanoes and centers of volcanism on Venus. In S. W. Bougher, D. M. Hunten, and R. J. Phillips, (Ed.), Venus II. University of Arizona Press.
Duda, R.O. & Hart, P.E. (1973). Pattern Classification and Scene Analysis. New York, NY: JohnWiley and Sons, New York.
Fayyad, U.M., Smyth, P., Burl, M.C. & Perona, P. (1996). A learning approach to object recognition: applications in science image analysis. In S. Nayar and T. Poggio, (Ed.), Early Visual Learning. New York, NY: Oxford University Press.
Flickner, M., Sawhney, H., Niblack, W., Ashley, J., Huang, Q., Dom, B., Gorkani, M., Hafner, J., Lee, D. et al. (1995). Query by image and video content – the QBIC system. Computer, 28(9):23-32.
Fukunaga, K. (1990). Introduction to Statistical Pattern Recognition. Second Edition, Academic Press.
Green, D.M. & Swets, J.A. (1966). Signal Detection Theory and Psychophysics. New York: Wiley.
Guest, J.E., Bulmer, M.H., Aubele, J., Beratan, K., Greeley, R., Head, J., Michaels, G., Weitz, C. & Wiles, C. (1992). Small volcanic edifices and volcanism in the plains of Venus. Journal of Geophysical Research Planets, 97(E10):15949-15966.
Kubat, M., Holte, R. & Matwin, S. (1998). Machine learning for the detection of oil spills in satellite radar images. Machine Learning, 30, 195-215.
Langley, P. & Simon, H.A. (1995). Applications of machine learning and rule induction. Communications of the ACM, 38(11):55-64.
MacMillan, N.A. & Creelman, C.D. (1991). Signal Detection Theory: A User' Guide. New York: Cambridge Univesity Press.
Mendel, E., et al. (1997). SAOimage: the next generation. Smithsonian Astrophysical Society, version 1.7.
Moghaddam, B. & Pentland, A. (1995). Maximum likelihood detection of faces and hands. International Workshop on Automatic Face and Gesture Recognition (pp. 122-128). Zurich, Switzerland.
Pentland, A., Picard, R.W. & Sclaroff, S. (1996). Photobook-content-based manipulation of image databases. International Journal of Computer Vision, 18(3):233-254.
Pettengill, G.H., Ford, P.G., Johnson, W.T.K., Raney, R.K. & Soderblom, L.A. (1991). Magellan: Radar Performance and Data Products. Science, 252:260-265.
Picard, R.W. & Pentland, A.P. (1996). Introduction to the special section on digital libraries: representation and retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(8):769-853.
Poulton, E.C. (1994). Behavioral Decision Theory: A New Approach. New York, NY: Cambridge University Press.
Provost, F. & Fawcett, T. (1997). Analysis and visualization of classifier performance: comparison under imprecise class and cost distributions. Proceedings of the Third International Conference on Knowledge Discovery and Data Mining (pp. 43-48). Newport Beach, CA: AAAI Press.
Richards, J.A. (1986). Remote Sensing for Digital Image Analysis. Berlin: Springer-Verlag.
Saunders, R.S., Spear, A.J., Allin, P.C., Austin, R.S., Berman, A.L., Chandlee, R.C., Clark, J., Decharon, A.V. & Dejong, E.M. (1992). Magellan Mission Summary. Journal of Geophysical Research Planets, 97(E8):13067-13090.
Simard, P., le Cun, Y. & Denker, J. (1993). Efficient pattern recognition using a new transformation distance. Advances in Neural Information Processing Systems 5 (pp. 50-58).
Sirovich, L. & Kirby, M. (1987). Low dimensional procedure for the characterization of human faces. Journal of Optical Society of America, 4(3):519-524.
Skingley, J. & Rye, A.J. (1987). The Hough transform applied to SAR images for thin line detection. Pattern Recognition Letters, 6:61-67.
Spackman, K.A. (1989). Signal detection theory: valuable tools for evaluating inductive learning. Proceedings of the Sixth International Workshop on Machine Learning (pp. 160-163). San Mateo, CA: Morgan Kaufman.
Stofan, E.R., Sharpton, V.L., Schubert, G., Baer, G., Bindschadler, D.L., Janes, D.M. & Squyres, S.W. (1992). Global distribution and characteristics of coronae and related features on Venus – Implications for origin and relation to mantle processes. Journal of Geophysical Research Planets, 97(E8):13347-13378.
Stonebraker, M. & Kemnitz, G. (1991). The POSTGRES next generation database-management system. Communications of the ACM 34(10):78-92.
Stough, T. & Brodley, C. (1997). Image feature reduction through spoiling: its application to multiple matched filters for focus of attention. Proceedings of the Third Annual Conference on Knowledge Discovery and Data Mining (pp. 255-259). Newport Beach, CA: AAAI Press
Swets, D.L. & Weng, J. (1996). Using discriminant eigenfeatures for image retrieval. Pattern Analysis and Machine Intelligence, 18(8):831-836.
Treitel, S. & Shanks, J. (1971). The design of multistage separable planar filters. IEEE Transactions on Geoscience and Electronics, 9(1):10-27.
Turk, M. & Pentland, A. (1991). Eigenfaces for recognition. Journal of Cognitive Neuroscience, 3(1):71-86.
Turmon, M. (1996). Personal communication.
Wiles, C.R. & Forshaw, M.R.B. (1993). Recognition of volcanoes using correlation methods. Image and Vision Computing, 11(4):188-196.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Burl, M.C., Asker, L., Smyth, P. et al. Learning to Recognize Volcanoes on Venus. Machine Learning 30, 165–194 (1998). https://doi.org/10.1023/A:1007400206189
Issue Date:
DOI: https://doi.org/10.1023/A:1007400206189