Optimal Sub-Shape Models by Minimum Description Length

G. Langs, P. Peloschek, H. Bischof
2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)  
Active shape models are a powerful and widely used tool to interpret complex image data. By building models of shape variation they enable search algorithms to use a priori knowledge in an efficient and gainful way. However, due to the linearity of PCA, non-linearities like rotations or independently moving sub-parts in the data can deteriorate the resulting model considerably. Although non-linear extensions of active shape models have been proposed and application specific solutions have been
more » ... sed, they still need a certain amount of user interaction during model building. In this paper the task of building/choosing optimal models is tackled in a more generic information theoretic fashion. In particular, we propose an algorithm based on the minimum description length principle to find an optimal subdivision of the data into sub-parts, each adequate for linear modeling. This results in an overall more compact model configuration. Which in turn leads to a better model in terms of modes of variations. The proposed method is evaluated on synthetic data, medical images and hand contours. *
doi:10.1109/cvpr.2005.265 dblp:conf/cvpr/LangsPB05 fatcat:njaf6y3ihbhxxnk5pyozvtcph4