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Object Classification Using Simple, Colour Based Visual Attention and a Hierarchical Neural Network for Neuro-symbolic Integration

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KI-99: Advances in Artificial Intelligence (KI 1999)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1701))

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Abstract

An object classification system built of a simple colour based visual attention method, and a prototype based hierarchical classifier is established as a link between subsymbolic and symbolic data processing. During learning the classifier generates a hierarchy of prototypes. These prototypes constitute a taxonomy of objects. By assigning confidence values to the prototypes a classification request may also return symbols with confidence values.

For performance evaluation the classifier was applied to the task of visual object categorization of three data sets, two real—world and one artificial. Orientation histograms on subimages were utilized as features.With the currently very simple feature extraction method, classification accuracies in the range of 69% to 90% were attained.

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Author information

Authors and Affiliations

  1. Department of Neural Information Processing, University of Ulm, 89069, Ulm, Germany

    Hans A. Kestler, Steffen Simon, Axel Baune, Friedhelm Schwenker & Günther Palm

Authors
  1. Hans A. Kestler
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  2. Steffen Simon
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  3. Axel Baune
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  4. Friedhelm Schwenker
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  5. Günther Palm
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Editor information

Editors and Affiliations

  1. Institut für Informatik III, Universität Bonn, Römerstraße 164, D-53117, Bonn, Germany

    Wolfram Burgard  & Armin B. Cremers  & 

  2. GMD-Forschungszentrum Informationstechnik GmbH, Schloß Birlinghoven, D-53754, Sankt Augustin, Germany

    Thomas Cristaller

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© 1999 Springer-Verlag Berlin Heidelberg

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Kestler, H.A., Simon, S., Baune, A., Schwenker, F., Palm, G. (1999). Object Classification Using Simple, Colour Based Visual Attention and a Hierarchical Neural Network for Neuro-symbolic Integration. In: Burgard, W., Cremers, A.B., Cristaller, T. (eds) KI-99: Advances in Artificial Intelligence. KI 1999. Lecture Notes in Computer Science(), vol 1701. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48238-5_22

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  • DOI: https://doi.org/10.1007/3-540-48238-5_22

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-66495-6

  • Online ISBN: 978-3-540-48238-3

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