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Coevolution and Linear Genetic Programming for Visual Learning
Krzysztof Krawiec and Bir Bhanu*
Center for Research in Intelligent Systems
University of California,
Riverside, CA 92521-0425, USA
{kkrawiec,bhanu}@cris.ucr.edu
Abstract. In this paper, a novel genetically-inspired visual learning method is proposed. Given the training images, this general approach induces a sophisticated feature-based recognition system, by using cooperative coevolution and linear genetic programming for the procedural representation of feature extraction agents. The paper describes the learning algorithm and provides a firm rationale for its design. An extensive experimental evaluation, on the demanding real-world task of object recognition in synthetic aperture radar (SAR) imagery, shows the competitiveness of the proposed approach with human-designed recognition systems.
*
On a temporary leave from Institute of Computing Science, Poznan University of Technology, Poznan, Poland.
LNCS 2723, p. 332 ff.
lncs@springer.de
© Springer-Verlag Berlin Heidelberg 2003