GAMM: genetic algorithms with meta-models for vision

ABSTRACT

Recent adaptive image interpretation systems can reach optimal performance for a given domain via machine learning, without human intervention. The policies are learned over an extensive generic image processing operator library. One of the principal weaknesses of the method lies with the large size of such libraries, which can make the machine learning process intractable. We demonstrate how evolutionary algorithms can be used to reduce the size of the operator library, thereby speeding up learning of the policy while still keeping human experts out of the development loop. Experiments in a challenging domain of forestry image interpretation exhibited a 95% reduction in the average time required to interpret an image, while maintaining the image interpretation accuracy of the full library.

REFERENCES

• 1: Darius S. Culvenor, TIDA: an algorithm for the delineation of tree crowns in high spatial resolution remotely sensed imagery, Computers & Geosciences, v.28 n.1, p.33-44, February 2002 [doi>10.1016/S0098-3004(00)00110-2 ]
• 2: B. Draper, J. Bins, and K. Baek. ADORE: adaptive object recognition. Videre, (4):86--99, 2000.
• 3: B. A. Draper. From knowledge bases to Markov models to PCA. In Proceedings of Workshop on Computer Vision System Control Architectures, Austria, 2003.
• 4: C. Emmanouilidis, A. Hunter, J. MacIntyre, and C. Cox. Multiple Criteria Genetic Algorithms for Feature Selection in Neurofuzzy Modeling. In In Proceedings of IJCNN, Washington, D.C., 1999.
• 5: F. Gougeon and D. Leckie. Forest information extraction from high spatial resolution images using an individual tree crown approach. Technical report, Pacific Forestry Centre, 2003.
• 6: J. Jarmulak and S. Craw. Genetic algorithms for feature selection and weighting. In In Proceedings of the IJCAI'99 workshop on Automating the Construction of Case Based Reasoners, pages 28--33, 1999.
• 7: Y. Jin, M. Olhofer, and B. Sendhoff. Managing approximate models in evolutionary aerodynamic design optimization. In Proceedings of the 2001 Congress on Evolutionary Computation CEC2001, pages 592--599, COEX, World Trade Center, 159 Samseong-dong, Gangnam-gu, Seoul, Korea, 27-30 May 2001. IEEE Press.
• 8: K. Kira and L. Rendell. The feature selection problem: Traditional methods and a new algorithm. In Proceedings of the Tenth National Conference on Artificial Intelligence (AAAI-92), pages 129--134, 1992.
• 9: Ron Kohavi , George H. John, Wrappers for feature subset selection, Artificial Intelligence, v.97 n.1-2, p.273-324, Dec. 1997 [doi>10.1016/S0004-3702(97)00043-X ]
• 10: G. Lee. Automated action set selection in Markov decision processes. Master's thesis, Department of Computing Science, University of Alberta, 2004.
• 11: I. Levner and V. Bulitko. Machine learning for adaptive image interpretation. In Proceedings of the 16th Innovative Applications of Artificial Intelligence'04 conference, 2004.
• 12: Luiz E. Soares de Oliveira , N. Benahmed , Robert Sabourin , Flavio Bortolozzi , Ching Y. Suen, Feature Subset Selection Using Genetic Algorithms for Handwritten Digit Recognition, Proceedings of the 14th Brazilian Symposium on Computer Graphics and Image Processing, p.362, October 15-18, 2001
• 13: R. Pollock. A model-based approach to automatically locating tree crowns in high spatial resolution images. In J. Desachy, editor, Image and Signal Processing for Remote Sensing, 1994.
• 14: P. Pudil , J. Novovičová , J. Kittler, Floating search methods in feature selection, Pattern Recognition Letters, v.15 n.11, p.1119-1125, Nov. 1994 [doi>10.1016/0167-8655(94)90127-9]
• 15: Z. Sun, X. Yuan, G. Bebis, and S. Louis. Neural-network-based gender classification using genetic eigen-feature extraction. In In Proceedings of IEEE International Joint Conference on Neural Networks, Honoloulu, Hawaii, 2002.
• 16: Richard S. Sutton , Andrew G. Barto, Introduction to Reinforcement Learning, MIT Press, Cambridge, MA, 1998
• 17: H. Vafaie and K. D. Jong. Genetic algorithms as a tool for feature selection in machine learning. In In Proceeding of the 4th International Conference on Tools with Artificial Intelligence, pages 200--204, Arlington, VA, 1992.
• 18: H. Vafaie and K. D. Jong. Robust feature selection algorithms. In In Proceedings of the Fifth Conference on Tools for Artificial Intelligence, pages 356--363, Boston, MA, 1993. IEEE Computer Society Press.
• 19: P. Viola and M. Jones. Fast and robust classification using asymmetric adaboost and a detector cascade. In T. G. Dietterich, S. Becker, and Z. Ghahramani, editors, Advances in Neural Information Processing Systems 14, MA, 2002. MIT Press.
• 20: C. Watkins. Learning from Delayed Rewards. PhD thesis, King's College, University of Cambridge, UK, 1989.

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.4 IMAGE PROCESSING AND COMPUTER VISION
      I.4.8 Scene Analysis

General Terms:
Performance

Keywords:
Markov decision process, adaptive image interpretation, genetic algorithms, heuristic search, machine learning