In this paper, we present a novel approach to genetic learning of high-level visual concepts that works with sets of attributed visual primitives rather than with raster images. The paper presents the approach in detail and verifies it in an experiment concerning locating objects in real-world 3D scenes.
@INPROCEEDINGS { Krawiec06a,
ABSTRACT = { In this paper, we present a novel approach to genetic learning of high-level visual concepts that works with sets of attributed visual primitives rather than with raster images. The paper presents the approach in detail and verifies it in an experiment concerning locating objects in real-world 3D scenes. },
ADDRESS = { Berlin Heidelberg },
AUTHOR = { Krzysztof Krawiec },
BOOKTITLE = { EvoWorkshops 2006 },
EDITOR = { F. R. },
FILE = { http://www.cs.put.poznan.pl/kkrawiec/pubs/2006EVOIASPposter.pdf },
PAGES = { 515--519 },
PUBLISHER = { Springer-Verlag },
SERIES = { {LNCS} 3907 },
TITLE = { Learning High-Level Visual Concepts Using Attributed Primitives and Genetic Programming },
URL = { http://dx.doi.org/10.1007/11732242_48 },
YEAR = { 2006 },
1 = { http://dx.doi.org/10.1007/11732242_48 },
}