A generalized possibilistic approach to shell clustering of template-based shapes

Tsaipei Wang; Wen Liang Hung

doi:10.1080/00949655.2016.1209202

A generalized possibilistic approach to shell clustering of template-based shapes

Tsaipei Wang, Wen Liang Hung^*

^*Corresponding author for this work

Institute of Multimedia Engineering

Research output: Contribution to journal › Article › peer-review

6 Scopus citations

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.

Original language	English
Pages (from-to)	423-436
Number of pages	14
Journal	Journal of Statistical Computation and Simulation
Volume	87
Issue number	3
DOIs	https://doi.org/10.1080/00949655.2016.1209202
State	Published - 11 Feb 2017

Keywords

Diversity index
generic-template-based shape detection
possibilistic shell clustering
power-law relation

Access to Document

10.1080/00949655.2016.1209202

Cite this

@article{9296e393ad804358b3e51f1f962999ff,

title = "A generalized possibilistic approach to shell clustering of template-based shapes",

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 {\textquoteleft}grade of detection{\textquoteright} gd for each target cluster; (ii) the amount of computation, denoted as kc, required to attain a given gd.",

keywords = "Diversity index, generic-template-based shape detection, possibilistic shell clustering, power-law relation",

author = "Tsaipei Wang and Hung, {Wen Liang}",

note = "Publisher Copyright: {\textcopyright} 2016 Informa UK Limited, trading as Taylor & Francis Group.",

year = "2017",

month = feb,

day = "11",

doi = "10.1080/00949655.2016.1209202",

language = "English",

volume = "87",

pages = "423--436",

journal = "Journal of Statistical Computation and Simulation",

issn = "0094-9655",

publisher = "Taylor and Francis Ltd.",

number = "3",

}

TY - JOUR

T1 - A generalized possibilistic approach to shell clustering of template-based shapes

AU - Wang, Tsaipei

AU - Hung, Wen Liang

PY - 2017/2/11

Y1 - 2017/2/11

N2 - 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’ gd for each target cluster; (ii) the amount of computation, denoted as kc, required to attain a given gd.

AB - 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’ gd for each target cluster; (ii) the amount of computation, denoted as kc, required to attain a given gd.

KW - Diversity index

KW - generic-template-based shape detection

KW - possibilistic shell clustering

KW - power-law relation

UR - http://www.scopus.com/inward/record.url?scp=84978496768&partnerID=8YFLogxK

U2 - 10.1080/00949655.2016.1209202

DO - 10.1080/00949655.2016.1209202

M3 - Article

AN - SCOPUS:84978496768

SN - 0094-9655

VL - 87

SP - 423

EP - 436

JO - Journal of Statistical Computation and Simulation

JF - Journal of Statistical Computation and Simulation

IS - 3

ER -

A generalized possibilistic approach to shell clustering of template-based shapes

Abstract

Keywords

Access to Document

Other files and links

Fingerprint

Cite this