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Robust Tensor Classifiers for Color Object Recognition

  • Christian Bauckhage
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4633)

Abstract

This paper presents an extension of linear discriminant analysis to higher order tensors that enables robust color object recognition. Given a labeled sample of training images, the basic idea is to consider a parallel factor model of a corresponding projection tensor. In contrast to other recent approaches, we do not compute a higher order singular value decomposition of the optimal projection. Instead, we directly derive a suitable approximation from the training data. Applying an alternating least squares procedure to repeated tensor contractions allows us to compute templates or binary classifiers alike. Moreover, we show how to incorporate a regularization method and the kernel trick in order to better cope with variations in the data. Experiments on face recognition from color images demonstrate that our approach performs very reliably, even if just a few examples are available for training.

Keywords

Face Recognition Linear Discriminant Analysis Training Image Alternate Little Square Projection Tensor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Christian Bauckhage
    • 1
  1. 1.Deutsche Telekom Laboratories 10587 BerlinGermany

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