Learning Compositional Categorization Models

  • Björn Ommer
  • Joachim M. Buhmann
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3953)


This contribution proposes a compositional approach to visual object categorization of scenes. Compositions are learned from the Caltech 101 database and form intermediate abstractions of images that are semantically situated between low-level representations and the high-level categorization. Salient regions, which are described by localized feature histograms, are detected as image parts. Subsequently compositions are formed as bags of parts with a locality constraint. After performing a spatial binding of compositions by means of a shape model, coupled probabilistic kernel classifiers are applied thereupon to establish the final image categorization. In contrast to the discriminative training of the categorizer, intermediate compositions are learned in a generative manner yielding relevant part agglomerations, i.e. groupings which are frequently appearing in the dataset while simultaneously supporting the discrimination between sets of categories. Consequently, compositionality simplifies the learning of a complex categorization model for complete scenes by splitting it up into simpler, sharable compositions. The architecture is evaluated on the highly challenging Caltech 101 database which exhibits large intra-category variations. Our compositional approach shows competitive retrieval rates in the range of 53.6 ± 0.88% or, with a multi-scale feature set, rates of 57.8 ± 0.79%.


Training Image Local Descriptor Category Label Salient Region Retrieval Rate 
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 2006

Authors and Affiliations

  • Björn Ommer
    • 1
  • Joachim M. Buhmann
    • 1
  1. 1.Institute of Computational ScienceETH ZurichZurichSwitzerland

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