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Background Modeling via Incremental Maximum Margin Criterion

  • Cristina Marghes
  • Thierry Bouwman
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6469)

Abstract

Subspace learning methods are widely used in background modeling to tackle illumination changes. Their main advantage is that it doesn’t need to label data during the training and running phase. Recently, White et al. [1] have shown that a supervised approach can improved significantly the robustness in background modeling. Following this idea, we propose to model the background via a supervised subspace learning called Incremental Maximum Margin Criterion (IMMC). The proposed scheme enables to initialize robustly the background and to update incrementally the eigenvectors and eigenvalues. Experimental results made on the Wallflower datasets show the pertinence of the proposed approach.

Keywords

Principal Component Analysis Linear Discriminant Analysis Independent Component Analysis Background Modeling Independent Component Analysis 
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 2011

Authors and Affiliations

  • Cristina Marghes
    • 1
  • Thierry Bouwman
    • 1
  1. 1.Laboratoire MIAUniversity of La RochelleLa RochelleFrance

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