Krzysztof Krawiec


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The paper presents a novel method of evolutionary learning dedicated to acquisition of visual concepts. The learning process takes place in a population of Genetic Programming-based learners that process attributed visual primitives derived from raw raster images. The approach uses an original evaluation scheme: evolving individuals-learners are rewarded for being able to sketch the input visual stimulus. Recognition proceeds here as an attempt of restoring essential features of the input image. The approach is general by being based mostly on universal vision knowledge; only very limited amount of a priori knowledge about the particular application or target concept to be learned is required. We explain the method in detail and verify it experimentally on acquisition of simple visual concepts (triangle and section) from examples.

@INPROCEEDINGS { Krawiec06b,
    AUTHOR = { Krzysztof Krawiec },
    TITLE = { Evolutionary Learning of Primitive-Based Visual Concepts },
    BOOKTITLE = { Proc. {IEEE} Congress on Evolutionary Computation, Sheraton Vancouver Wall Centre Hotel, Vancouver, BC, Canada July 16-21 },
    YEAR = { 2006 },
    PAGES = { 4451--4458 },
    ABSTRACT = { The paper presents a novel method of evolutionary learning dedicated to acquisition of visual concepts. The learning process takes place in a population of Genetic Programming-based learners that process attributed visual primitives derived from raw raster images. The approach uses an original evaluation scheme: evolving individuals-learners are rewarded for being able to sketch the input visual stimulus. Recognition proceeds here as an attempt of restoring essential features of the input image. The approach is general by being based mostly on universal vision knowledge; only very limited amount of a priori knowledge about the particular application or target concept to be learned is required. We explain the method in detail and verify it experimentally on acquisition of simple visual concepts (triangle and section) from examples. },
    URL = { http://ieeexplore.ieee.org/iel5/11108/35623/01688460.pdf },
}


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