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International Conference on Artificial Neural Networks

ICANN 2014: Artificial Neural Networks and Machine Learning – ICANN 2014 pp 459–466Cite as

How Visual Attention and Suppression Facilitate Object Recognition?

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8681))

Abstract

Visual attention can support object recognition by selecting the relevant target information in the huge amount of sensory data, especially important in scenes composed of multiple objects. Here we demonstrate how attention in a biologically plausible and neuro-computational model of visual perception facilitates object recognition in a robotic real world scenario. We will point out that it is not only important to select the target information, but rather to explicitly suppress the distracting sensory data. We found that suppressing the features of each distractor is not sufficient to achieve robust recognition. Instead, we also have to suppress the location of each distractor. To demonstrate the effect of this spatial suppression, we disable this property and show that the recognition accuracy drops. By this, we show the interplay between attention and suppression in a real world object recognition task.

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Author information

Authors and Affiliations

  1. Artificial Intelligence, Chemnitz University of Technology, Strasse der Nationen 62, 09111, Chemnitz, Germany

    Frederik Beuth, Amirhossein Jamalian & Fred H. Hamker

Authors
  1. Frederik Beuth
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  2. Amirhossein Jamalian
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  3. Fred H. Hamker
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Editor information

Editors and Affiliations

  1. Department of Informatics, University of Hamburg, Vogt-Kölln-Straße 30, 22527, Hamburg, Germany

    Stefan Wermter , Cornelius Weber  & Sven Magg ,  & 

  2. Department of Informatics, Nicolaus Compernicus University, ul. Grudziądzka 5, 87-100, Torun, Poland

    Włodzisław Duch

  3. Department of Modern Languages, University of Helsinki, P.O. Box 24, 00014, Helsinki, Finland

    Timo Honkela

  4. Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Acad. G. Bonchev str. bl. 25A, 1113, Sofia, Bulgaria

    Petia Koprinkova-Hristova

  5. Institute of Neural Information Processing, University of Ulm, 89069, Oberer Eselsberg, Ulm, Germany

    Günther Palm

  6. Department of Information Systems, Quartier UNIL-Dorigny, Bâtiment Internef, University of Lausanne, 1015, Lausanne, Switzerland

    Alessandro E. P. Villa

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© 2014 Springer International Publishing Switzerland

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Beuth, F., Jamalian, A., Hamker, F.H. (2014). How Visual Attention and Suppression Facilitate Object Recognition?. In: Wermter, S., et al. Artificial Neural Networks and Machine Learning – ICANN 2014. ICANN 2014. Lecture Notes in Computer Science, vol 8681. Springer, Cham. https://doi.org/10.1007/978-3-319-11179-7_58

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  • DOI: https://doi.org/10.1007/978-3-319-11179-7_58

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11178-0

  • Online ISBN: 978-3-319-11179-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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