Possibilistic shell clustering of template-based shapes

Tsaipei Wang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


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.

Original languageEnglish
Pages (from-to)777-793
Number of pages17
JournalIEEE Transactions on Fuzzy Systems
Issue number4
StatePublished - 21 Aug 2009


  • Alternating optimization (AO)
  • Object detection
  • Possibilistic clustering
  • Progressive clustering
  • Shape detection
  • Shell clustering
  • Template matching

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