Krzysztof Krawiec


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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.

@INPROCEEDINGS { KrawiecBhanu03a,
    AUTHOR = { K. Krawiec and B. Bhanu },
    TITLE = { Coevolution and Linear Genetic Programming for Visual Learning },
    BOOKTITLE = { Genetic and Evolutionary Computation },
    PUBLISHER = { Springer-Verlag },
    YEAR = { 2003 },
    EDITOR = { E. Cant{\'u}-Paz and and others },
    VOLUME = { 2723 },
    SERIES = { Lecture Notes in Computer Science },
    PAGES = { 332--343 },
    ADDRESS = { Chicago, {IL}, July 12-16 },
    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. },
}


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