An integrated system for incremental learning of multiple visual categories

Authors:
Stephan Kirstein;Heiko Wersing;Horst-Michael Gross;Edgar Körner
Affiliations:
Ilmenau University of Technology, Neuroinformatics and Cognitive Robotics Lab, Ilmenau, Germany and Honda Research Institute Europe GmbH, Offenbach am Main, Germany;Honda Research Institute Europe GmbH, Offenbach am Main, Germany;Ilmenau University of Technology, Neuroinformatics and Cognitive Robotics Lab, Ilmenau, Germany;Honda Research Institute Europe GmbH, Offenbach am Main, Germany
Venue:
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
Year:
2008

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Abstract

We present a biologically inspired vision system able to incrementally learn multiple visual categories by interactively presenting several hand-held objects. The overall systemis composed of a foreground-background separation part, several feature extraction methods and a life-long learning approach combining incremental learning with category specific feature selection. In contrast to most visual categorization approaches where typically each view is assigned to a single category we allow labeling with an arbitrary number of shape and color categories and also impose no restrictions to the viewing angle of presented objects.