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International Workshop on Biologically Motivated Computer Vision

BMCV 2000: Biologically Motivated Computer Vision pp 535–544Cite as

Topographic ICA as a Model of Natural Image Statistics

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1811))

Abstract

Independent component analysis (ICA), which is equivalent to linear sparse coding, has been recently used as a model of natural image statistics and V1 receptive fields. Olshausen and Field applied the principle of maximizing the sparseness of the coefficients of a linear representation to extract features from natural images. This leads to the emergence of oriented linear filters that have simultaneous localization in space and in frequency, thus resembling Gabor functions and V1 simple cell receptive fields. In this paper, we extend this model to explain emergence of V1 topography. This is done by ordering the basis vectors so that vectors with strong higher-order correlations are near to each other. This is a new principle of topographic organization, and may be more relevant to natural image statistics than the more conventional topographic ordering based on Euclidean distances. For example, this topographic ordering leads to simultaneous emergence of complex cell properties: neighbourhoods act like complex cells.

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Author information

Authors and Affiliations

  1. Neural Networks Research Centre, Helsinki University of Technology, P.O. Box 5400, FIN-02015, Hut, Finland

    Aapo Hyvärinen, Patrik O. Hoyer & Mika Inki

Authors
  1. Aapo Hyvärinen
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  2. Patrik O. Hoyer
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  3. Mika Inki
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Editor information

Editors and Affiliations

  1. Center for Artificial Vision Research, Korea University, Anam-dong, Seongbuk-ku, Seoul, 136-701, Korea

    Seong-Whan Lee

  2. Max-Planck-Institute for Biological Cybernetics, Spemannstr. 38, 72076, Tübingen, Germany

    Heinrich H. Bülthoff

  3. Department of Brain and Cognitive Sciences Artificial Intelligence Laboratory, E25-218, Massachusetts Institute of Technology, 45 Carleton Street, Cambridge, MA, 02142, USA

    Tomaso Poggio

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© 2000 Springer-Verlag Berlin Heidelberg

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Hyvärinen, A., Hoyer, P.O., Inki, M. (2000). Topographic ICA as a Model of Natural Image Statistics. In: Lee, SW., Bülthoff, H.H., Poggio, T. (eds) Biologically Motivated Computer Vision. BMCV 2000. Lecture Notes in Computer Science, vol 1811. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45482-9_54

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  • DOI: https://doi.org/10.1007/3-540-45482-9_54

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-67560-0

  • Online ISBN: 978-3-540-45482-3

  • eBook Packages: Springer Book Archive

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