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Unsupervised Learning of Models for Recognition

  • M. Weber
  • M. Welling
  • P. Perona
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1842)

Abstract

We present a method to learn object class models from unlabeled and unsegmented cluttered scenes for the purpose of visual object recognition. We focus on a particular type of model where objects are represented as flexible constellations of rigid parts (features). The variability within a class is represented by a joint probability density function (pdf) on the shape of the constellation and the output of part detectors. In a first stage, the method automatically identifies distinctive parts in the training set by applying a clustering algorithm to patterns selected by an interest operator. It then learns the statistical shape model using expectation maximization. The method achieves very good classification results on human faces and rear views of cars.

Keywords

Expectation Maximization Training Image Object Class Unsupervised Learn Expectation Maximization Algorithm 
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 2000

Authors and Affiliations

  • M. Weber
    • 1
  • M. Welling
    • 2
  • P. Perona
    • 1
    • 2
    • 3
  1. 1.Dept. of Computation and Neural SystemsCalifornia Institute of TechnologyPasadenaUSA
  2. 2.Dept. of Electrical EngineeringCalifornia Institute of TechnologyPasadenaUSA
  3. 3.Universita di PadovaItaly

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