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Adaptive Feature Transformation for Image Data from Non-stationary Processes

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Artificial Neural Networks – ICANN 2009 (ICANN 2009)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5769))

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Abstract

This paper introduces the application of the feature transformation approach proposed by Torkkola [1] to the domain of image processing. Thereto, we extended the approach and identifed its advantages and limitations.

We compare the results with more common transformation methods like Principal Component Analysis and Linear Discriminant Analysis for a function approximation task from the challenging domain of video-based combustion optimization. It is demonstrated that the proposed method generates superior results in very low dimensional subspaces.

Further, we investigate the usefulness of an adaptive variant of the introduced method in comparison to basic subspace transformations and discuss the results.

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References

  1. Torkkola, K.: Feature Extraction by Non Parametric Mutual Information Maximization. Journal of Machine Learning Research 3, 1415–1438 (2003)

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Author information

Authors and Affiliations

  1. Neuroinformatics and Cognitive Robotics Lab, Ilmenau University of Technology, 98693, Ilmenau, Germany

    Erik Schaffernicht & Horst-Michael Gross

  2. Powitec Intelligent Technologies GmbH, 45219, Essen-Kettwig, Germany

    Volker Stephan

Authors
  1. Erik Schaffernicht
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  2. Volker Stephan
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  3. Horst-Michael Gross
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Editor information

Editors and Affiliations

  1. Dipartimento di Elettronica, Politecnico di Milano, Piazza L. da Vinci 32, 20133, Milano, Italy

    Cesare Alippi

  2. Department of Electrical and Computer Engineering, University of Cyprus, 75 Kallipoleos Street, 1678, Nicosia, Cyprus

    Marios Polycarpou , Christos Panayiotou  & Georgios Ellinas ,  & 

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© 2009 Springer-Verlag Berlin Heidelberg

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Schaffernicht, E., Stephan, V., Gross, HM. (2009). Adaptive Feature Transformation for Image Data from Non-stationary Processes. In: Alippi, C., Polycarpou, M., Panayiotou, C., Ellinas, G. (eds) Artificial Neural Networks – ICANN 2009. ICANN 2009. Lecture Notes in Computer Science, vol 5769. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04277-5_74

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  • DOI: https://doi.org/10.1007/978-3-642-04277-5_74

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04276-8

  • Online ISBN: 978-3-642-04277-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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