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A Comparison of Features in Parts-Based Object Recognition Hierarchies

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Artificial Neural Networks – ICANN 2007 (ICANN 2007)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4669))

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Abstract

Parts-based recognition has been suggested for generalizing from few training views in categorization scenarios. In this paper we present the results of a comparative investigation of different feature types with regard to their suitability for category discrimination. So patches of gray-scale images were compared with SIFT descriptors and patches from the high-level output of a feedforward hierarchy related to the ventral visual pathway. We discuss the conceptual differences, resulting performance and consequences for hierarchical models of visual recognition.

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Authors and Affiliations

  1. Honda Research Institute Europe GmbH, D-63073 Offenbach, Germany

    Stephan Hasler, Heiko Wersing & Edgar Körner

Authors
  1. Stephan Hasler
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  2. Heiko Wersing
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  3. Edgar Körner
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Editor information

Joaquim Marques de Sá Luís A. Alexandre Włodzisław Duch Danilo Mandic

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Hasler, S., Wersing, H., Körner, E. (2007). A Comparison of Features in Parts-Based Object Recognition Hierarchies. In: de Sá, J.M., Alexandre, L.A., Duch, W., Mandic, D. (eds) Artificial Neural Networks – ICANN 2007. ICANN 2007. Lecture Notes in Computer Science, vol 4669. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74695-9_22

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  • DOI: https://doi.org/10.1007/978-3-540-74695-9_22

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-74693-5

  • Online ISBN: 978-3-540-74695-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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