We describe a novel method of evolutionary visual learning that uses generative approach for assessing learner's ability to recognize image contents. Each learner, implemented as a genetic programming individual, processes visualprimitivesthatrepresentlocalsalientfeatures derived from a raw input raster image. In response to that input, the learnerproducespartialreproductionoftheinputimage,andisevaluated according to the quality of that reproduction. We present the method in detail and verify it experimentally on the real-world task of recognition of hand-drawn shapes.
@INPROCEEDINGS { jaskowski07learning,
ABSTRACT = { We describe a novel method of evolutionary visual learning that uses generative approach for assessing learner's ability to recognize image contents. Each learner, implemented as a genetic programming individual, processes visualprimitivesthatrepresentlocalsalientfeatures derived from a raw input raster image. In response to that input, the learnerproducespartialreproductionoftheinputimage,andisevaluated according to the quality of that reproduction. We present the method in detail and verify it experimentally on the real-world task of recognition of hand-drawn shapes. },
ADDRESS = { Berlin Heidelberg },
AUTHOR = { Wojciech Ja{\'s}kowski and Krzysztof Krawiec and Bartosz Wieloch },
BOOKTITLE = { EvoWorkshops 2007 },
EDITOR = { M. Giacobini et al. },
OWNER = { krawiec },
PAGES = { 281-290 },
PUBLISHER = { Springer-Verlag },
SERIES = { LNCS },
TIMESTAMP = { 2007.02.19 },
TITLE = { Learning and Recognition of Hand-Drawn Shapes Using Generative Genetic Programming },
VOLUME = { 4448 },
YEAR = { 2007 },
}