A generalized possibilistic approach to shell clustering of template-based shapes. Issue 3 (11th February 2017)

• Record Type: Journal Article
• Authors: Wang, Tsaipei; Hung, Wen-Liang
• Is Part Of: Journal of statistical computation and simulation. Volume 87:Issue 3(2017)
• Page Start: 423
• Page End: 436
• Publication Date: 2017-02-11
• Subjects: Diversity index -- generic-template-based shape detection -- possibilistic shell clustering -- power-law relation; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5028505
• DOI: 10.1080/00949655.2016.1209202
• Languages: English
• ISSNs: 0094-9655

Abstract

ABSTRACT: Efficiently and effectively detecting shell-like structures of particular shapes is an important task in computer vision and image processing. This paper presents a generalized possibilistic c -means algorithm (PCM) for shell clustering based on the diversity index of degree- λ proposed by Patil and Taillie [Diversity as a concept and its measurement. J Amer Statist Assoc. 1982;77:548–561]. Experiments on various data sets in Wang [Possibilistic shell clustering of template-based shapes. IEEE Trans Fuzzy Syst. 2009;17:777–793] show that the the proposed generalized PCM performs better than Wang's [Possibilistic shell clustering of template-based shapes. IEEE Trans Fuzzy Syst. 2009;17:777–793] possibilistic shell clustering method according two two criteria: (i) the 'grade of detection' g d for each target cluster; (ii) the amount of computation, denoted as k c, required to attain a given g d .