A Context Dependent Distance Measure for Shape Clustering

Authors:
Rolf Lakaemper;Jingting Zeng
Affiliations:
CIS Department, Temple University, USA;CIS Department, Temple University, USA
Venue:
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing, Part II
Year:
2008

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Abstract

We present a new similarity measure between a single shape and a shape group as a basis for shape clustering following the paradigm of context dependent shape comparison: clusters are generated in the context of a reference shape, defined by the query shape it is compared to. Tightly coupled, the distance measure is the basis for a soft k -means like framework to achieve robust clustering. Successful application of the system along with generation of shape prototypes is demonstrated in comparison to latest approaches using elastic deformation.