標題: A Flexible Possibilistic C-Template Shell Clustering Method with Adjustable Degree of Deformation
作者: Wang, Tsaipei
資訊工程學系
Department of Computer Science
關鍵字: shell clustering;template-based clustering;possibilistic c-means;deformable templates
公開日期: 2016
摘要: 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.
URI: http://hdl.handle.net/11536/134565
ISBN: 978-1-5090-0625-0
ISSN: 1544-5615
期刊: 2016 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE)
起始頁: 1516
結束頁: 1522
Appears in Collections:Conferences Paper