Appearance Based Generic Object Modeling and Recognition Using Probabilistic Principal Component Analysis

  • Christopher Drexler
  • Frank Mattern
  • Joachim Denzler
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2449)


Classifying unknown objects in familiar, general categories rather than trying to classify them into a certain known, but only similar class, or rejecting them at all is an important aspect in object recognition. Especially in tasks, where it is impossible to model all possibly appearing objects in advance, generic object modeling and recognition is crucial.

We present a novel approach to generic object modeling and classification based on probabilistic principal component analysis (PPCA). A data set can be separated into classes during an unsupervised learning step using the expectation-maximization algorithm. In contrast to principal component analysis the feature space is modeled in a locally linear manner. Additionally, Bayesian classification is possible thanks to the underlying probabilistic model.

The approach is applied to the COIL-20/100 databases. It shows that PPCA is well suited for appearance based generic object modeling and recognition. The automatic, unsupervised generation of categories matches in most cases the categorization done by humans. Improvements are expected if the categorization is performed in a supervised fashion.


Near Neighbor Hierarchy Level Factor Analysis Model Unknown Object Category Match 
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 2002

Authors and Affiliations

  • Christopher Drexler
    • 1
  • Frank Mattern
    • 1
  • Joachim Denzler
    • 1
  1. 1.Lehrstuhl für MustererkennungUniversität Erlangen-NürnbergErlangen

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