Unsupervised Learning of Visual Feature Hierarchies

  • Fabien Scalzo
  • Justus Piater
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3587)


We propose an unsupervised, probabilistic method for learning visual feature hierarchies. Starting from local, low-level features computed at interest point locations, the method combines these primitives into high-level abstractions. Our appearance-based learning method uses local statistical analysis between features and Expectation-Maximization to identify and code spatial correlations. Spatial correlation is asserted when two features tend to occur at the same relative position of each other. This learning scheme results in a graphical model that constitutes a probabilistic representation of a flexible visual feature hierarchy. For feature detection, evidence is propagated using Belief Propagation. Each message is represented by a Gaussian mixture where each component represents a possible location of the feature. In experiments, the proposed approach demonstrates efficient learning and robust detection of object models in the presence of clutter and occlusion and under view point changes.


Graphical Model Visual Feature Spatial Relation Interest Point Primitive Feature 
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 2005

Authors and Affiliations

  • Fabien Scalzo
    • 1
  • Justus Piater
    • 1
  1. 1.Montefiore InstituteUniversity of LiègeLiègeBelgium

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