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Possibilistic Shell Clustering of Template-Based Shapes

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1 Author(s)
Tsaipei Wang ; Dept. of Comput. Sci., Nat. Chiao Tung Univ., Hsinchu, Taiwan

In this paper, we present a new type of alternating-optimization-based possibilistic c-shell algorithm for clustering-template-based shapes. A cluster prototype consists of a copy of the template after translation, scaling, rotation, and/or affine transformations. This extends the capability of shell clustering beyond a few standard geometrical shapes that have been in the literature so far. We use a number of 2-D datasets, consisting of both synthetic and real-world images, to illustrate the capability of our algorithm in detecting generic-template-based shapes in images. We also describe a progressive clustering procedure aimed to relax the requirements for a known number of clusters and good initialization, as well as new performance measures of shell-clustering algorithms.

Published in:

Fuzzy Systems, IEEE Transactions on  (Volume:17 ,  Issue: 4 )