| Authors |
Langs Georg, P. Peloschek, R. Donner, Bischof Horst |
| Appeared in |
Pattern Recognition |
| Volume |
40 |
| Number |
9 |
| Pages |
2485-2495 |
| Date |
2007 |
| 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 subdivision
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. |
| Link |
LINK
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