Template-based shell clustering using a line-segment representation of data

Tsai-Pei Wang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


This paper presents the algorithms and experimental results for template-based shell clustering when the datasets are represented by line segments. Compared with point datasets, such representations have several advantages, which include better scalability and noise immunity, as well as the availability of orientation information. Using both synthetic and real-world image datasets, we have experimentally demonstrated that line-segment-based representations result in both better accuracy and better efficiency in shell clustering.

Original languageEnglish
Article number5686926
Pages (from-to)575-580
Number of pages6
JournalIEEE Transactions on Fuzzy Systems
Issue number3
StatePublished - 1 Jun 2011


  • Line-segment approximation
  • line-segment matching
  • line-segment models
  • possibilistic c-means (PCMs)
  • shell clustering
  • template matching
  • template-based clustering

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