Multiple appearance models

Authors:
Georg Langs;Philipp Peloschek;René Donner;Horst Bischof
Affiliations:
ICG, Institute for Computer Graphics and Vision, Graz University of Technology, Inffeldgasse 16 2.OG, A-8010 Graz, Austria and Pattern Recognition and Image Processing Group, Vienna University of ...;Department of Radiology, Medical University of Vienna, Währinger Gürtel 18-20, A-1090 Vienna, Austria;ICG, Institute for Computer Graphics and Vision, Graz University of Technology, Inffeldgasse 16 2.OG, A-8010 Graz, Austria and Pattern Recognition and Image Processing Group, Vienna University of ...;ICG, Institute for Computer Graphics and Vision, Graz University of Technology, Inffeldgasse 16 2.OG, A-8010 Graz, Austria
Venue:
Pattern Recognition
Year:
2007

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Abstract

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.