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Memetic Computing

, 1:291 | Cite as

A vision architecture for unconstrained and incremental learning of multiple categories

  • Stephan Kirstein
  • Alexander Denecke
  • Stephan Hasler
  • Heiko Wersing
  • Horst-Michael Gross
  • Edgar Körner
Special Issue - Regular Research Paper

Abstract

We present an integrated vision architecture capable of incrementally learning several visual categories based on natural hand-held objects. Additionally we focus on interactive learning, which requires real-time image processing methods and a fast learning algorithm. The overall system is composed of a figure-ground segregation 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. We also impose no restrictions on the viewing angle of presented objects, relaxing the common constraint on canonical views.

Keywords

Real-time vision system Life-long learning Category learning Vector quantization Hand-held objects 

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Copyright information

© Springer-Verlag 2009

Authors and Affiliations

  • Stephan Kirstein
    • 1
    • 2
  • Alexander Denecke
    • 1
    • 3
  • Stephan Hasler
    • 1
  • Heiko Wersing
    • 1
  • Horst-Michael Gross
    • 2
  • Edgar Körner
    • 1
  1. 1.Honda Research Institute Europe GmbHOffenbach am MainGermany
  2. 2.Ilmenau University of Technology, Neuroinformatics and Cognitive Robotics LabIlmenauGermany
  3. 3.Bielefeld University, CoR-LabBielefeldGermany

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