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Reference
Will R. Smart and Mengjie Zhang: Classification Strategies for Image Classification in Genetic Programming. Proceeding of Image and Vision Computing NZ International Conference, pp. 402-407, Massey University, November 2003.
DOI
Abstract
This paper describes an approach to the use of genetic programming for multi-class image recognition problems. In this approach, the terminal set is constructed with image pixel statistics, the function set consists of arithmetic and conditional operators, and the fitness function is based on classification accuracy in the training set. Rather than using fixed static thresholds as boundaries to distinguish between different classes, this approach introduces two dynamic methods of classification, namely centred dynamic range selection and slotted dynamic range selection, based on the returned value of an evolved genetic program where the boundaries between different classes can be dynamically determined during the evolutionary process. The two dynamic methods are applied to five image datasets of classification problems of increasing difficulty and are compared with the commonly used static range selection method. The results suggest that, while the static boundary selection method works well on relatively easy binary or tertiary image classification problems with class labels arranged in the natural order, the two dynamic range selection methods outperform the static method for more difficult, multiple class problems.
Extended Abstract
Bibtex
Used References
[1] Wolfgang Banzhaf, Peter Nordin, Robert E. Keller, and Frank D. Francone. Genetic Programming: An Introduction on the Automatic Evolution of computer programs and its Applications. San Francisco, Calif.
- Morgan Kaufmann Publishers; Heidelburg : Dpunkt-
verlag, 1998. Subject: Genetic programming (Com- puter science); ISBN: 1-55860-510-X.
[2] John R. Koza. Genetic programming : on the pro- gramming of computers by means of natural selection. Cambridge, Mass. : MIT Press, London, England, 1992.
[3] Thomas Loveard and Victor Ciesielski. Representing classification problems in genetic programming. In Pro- ceedings of the Congress on Evolutionary Computation, volume 2, pages 1070–1077, COEX, World Trade Cen- ter, 159 Samseong-dong, Gangnam-gu, Seoul, Korea, 27-30 May 2001. IEEE Press.
[4] Andy Song, Vic Ciesielski, and Hugh Williams. Tex- ture classifiers generated by genetic programming. In David B. Fogel, Mohamed A. El-Sharkawi, Xin Yao, Garry Greenwood, Hitoshi Iba, Paul Marrow, and Mark Shackleton, editors, Proceedings of the 2002 Congress on Evolutionary Computation CEC2002, pages 243– 248. IEEE Press, 2002.
[5] Walter Alden Tackett. Genetic programming for feature discovery and image discrimination. In Stephanie Forrest, editor, Proceedings of the 5th International Conference on Genetic Algorithms, ICGA-93, pages 303–309, University of Illinois at Urbana-Champaign, 17-21 July 1993. Morgan Kaufmann.
[6] Mengjie Zhang and Victor Ciesielski. Genetic program- ming for multiple class object detection. In Norman Foo, editor, Proceedings of the 12th Australian Joint Conference on Artificial Intelligence (AI’99), pages 180–192, Sydney, Australia, December 1999. Springer- Verlag Berlin Heidelberg. Lecture Notes in Artificial Intelligence (LNAI Volume 1747).
[7] Mengjie Zhang, Peter Andreae, and Mark Pritchard. Pixel statistics and false alarm area in genetic program- ming for object detection. In Stefano Cagnoni, editor, Applications of Evolutionary Computing, Lecture Notes in Computer Science, LNCS Vol. 2611, pages 455–466. Springer-Verlag, 2003.
[8] Mengjie Zhang, Victor Ciesielski, and Peter Andreae. A domain independent window-approach to multiclass object detection using genetic programming. EURIASP Journal on Signal Processing, Special Issue on Genetic and Evolutionary Computation for Signal Processing and Image Analysis, 2003(8):841–859, 2003.
[9] Daniel Howard, Simon C. Roberts, and Richard Brankin. Target detection in SAR imagery by genetic programming. Advances in Engineering Software, 30:303–311, 1999.
[10] Jamie R. Sherrah, Robert E. Bogner, and Abdesse- lam Bouzerdoum. The evolutionary pre-processor: Automatic feature extraction for supervised classifica- tion using genetic programming. In John R. Koza, Kalyanmoy Deb, Marco Dorigo, David B. Fogel, Max Garzon, Hitoshi Iba, and Rick L. Riolo, editors, Genetic Programming 1997: Proceedings of the Second Annual Conference, pages 304–312, Stanford University, CA, USA, 13-16 July 1997. Morgan Kaufmann.
[11] Walter Alden Tackett. Recombination, Selection, and the Genetic Construction of Computer Programs. PhD thesis, Faculty of the Graduate School, University of Southern California, Canoga Park, California, USA, April 1994.
[12] John R. Koza. Genetic Programming II: Automatic Discovery of Reusable Programs. Cambridge, Mass.
- MIT Press, London, England, 1994.
Links
Full Text
http://www.mcs.vuw.ac.nz/~mengjie/papers/will-meng-ivcnz03.pdf
