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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-
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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
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303–309, University of Illinois at Urbana-Champaign,
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[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
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Programming 1997: Proceedings of the Second Annual
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[11] Walter Alden Tackett. Recombination, Selection, and
the Genetic Construction of Computer Programs. PhD
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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

[[intern file]]

=== Sonstige Links ===

@@ Zeile 27: / Zeile 27: @@
 and Frank D. Francone. Genetic Programming: An
 Introduction on the Automatic Evolution of computer
-programs and its Applications. San Francisco, Calif.
+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.
@@ Zeile 103: / Zeile 102: @@
 [12] John R. Koza. Genetic Programming II: Automatic
-Discovery of Reusable Programs. Cambridge, Mass.
+Discovery of Reusable Programs. Cambridge, Mass. MIT Press, London, England, 1994.
 == Links ==

Classification Strategies for Image Classification in Genetic Programming - Versionsgeschichte

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