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Multiple appearance models
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DOI：10.1016/j.patcog.2006.11.019.png)
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Abstract
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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. (c) 2006 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
Keywords:
MDL
model building
active appearance models
medical imaging
eigenimages
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