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<item>
  <id>05162508</id>
  <dt>j</dt>
  <an>05162508</an>
  <augroup>
    <au>Langs, Georg</au>
    <au>Peloschek, Philipp</au>
    <au>Donner, Ren\'e</au>
    <au>Bischof, Horst</au>
  </augroup>
  <ti>Multiple appearance models.</ti>
  <so>Pattern Recognition 40, No. 9, 2485-2495 (2007).</so>
  <py>2007</py>
  <pu>Elsevier Science Ltd. (Pergamon), Oxford</pu>
  <lagroup>
    <la>EN</la>
  </lagroup>
  <ccgroup>
  </ccgroup>
  <utgroup>
    <ut>MDL</ut>
    <ut>model building</ut>
    <ut>active appearance models</ut>
    <ut>medical imaging</ut>
    <ut>eigenimages</ut>
  </utgroup>
  <cigroup>
  </cigroup>
  <ligroup>
    <li>doi:10.1016/j.patcog.2006.11.019</li>
  </ligroup>
  <abgroup>
    <ab>Summary: This paper investigates a concept for modelling complex data based on sub-models. The task of building and choosing optimal models is addressed in a generic information theoretic fashion. We propose an algorithm based on minimum description length to find an optimal sub-division of the data into sub-parts, each adequate for linear modelling. This results in an overall more compact model configuration called a model clique and in better generalization behavior. The algorithm is applied to active appearance models, active shape models and eigenimages and is evaluated on 4 different data sets. Experiments indicate that model cliques exhibit better generalization behavior than single models and mimic intuitive sub-division of data.</ab>
    <rv></rv>
  </abgroup>
</item>