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A vision architecture for unconstrained and incremental learning of multiple categories

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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.

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Authors and Affiliations

  1. Honda Research Institute Europe GmbH, Carl-Legien-Str. 30, 63073, Offenbach am Main, Germany

    Stephan Kirstein, Alexander Denecke, Stephan Hasler, Heiko Wersing & Edgar Körner

  2. Ilmenau University of Technology, Neuroinformatics and Cognitive Robotics Lab, P. O. B. 10 05 65, 98684, Ilmenau, Germany

    Stephan Kirstein & Horst-Michael Gross

  3. Bielefeld University, CoR-Lab, P. O. B. 10 01 31, 33501, Bielefeld, Germany

    Alexander Denecke

Authors
  1. Stephan Kirstein
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  2. Alexander Denecke
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  3. Stephan Hasler
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  4. Heiko Wersing
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  5. Horst-Michael Gross
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  6. Edgar Körner
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Correspondence to Stephan Kirstein.

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Kirstein, S., Denecke, A., Hasler, S. et al. A vision architecture for unconstrained and incremental learning of multiple categories. Memetic Comp. 1, 291–304 (2009). https://doi.org/10.1007/s12293-009-0023-x

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  • DOI: https://doi.org/10.1007/s12293-009-0023-x

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