A flexible possibilistic c-template shell clustering method with adjustable degree of deformation

Tsaipei Wang*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper presents a new method of template based shell clustering that allows more flexible free deformation of the cluster prototypes with respect to the template-defined shapes. This is achieved via a soft division of the template into several template parts, each allowed to have its own set of transform parameters. A fuzzification factor, inspired by the one used in the standard fuzzy c-means algorithm, is used to control the degree of deformation by blending the transform parameters of the template parts. We demonstrate that this approach gives better shape detection and fitting results than the original possibilistic c-template algorithm using synthetic data of several different shapes.

Original languageEnglish
Title of host publication2016 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1516-1522
Number of pages7
ISBN (Electronic)9781509006250
DOIs
StatePublished - 7 Nov 2016
Event2016 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2016 - Vancouver, Canada
Duration: 24 Jul 201629 Jul 2016

Publication series

Name2016 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2016

Conference

Conference2016 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2016
CountryCanada
CityVancouver
Period24/07/1629/07/16

Keywords

  • Deformable templates
  • Possibilistic c-means
  • Shell clustering
  • Template-based clustering

Fingerprint Dive into the research topics of 'A flexible possibilistic c-template shell clustering method with adjustable degree of deformation'. Together they form a unique fingerprint.

Cite this