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

Tsai-Pei Wang

doi:10.1109/TFUZZ.2011.2105880

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

Tsai-Pei Wang^*

^*Corresponding author for this work

Department of Computer Science

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

2 Scopus citations

Abstract

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 language	English
Article number	5686926
Pages (from-to)	575-580
Number of pages	6
Journal	IEEE Transactions on Fuzzy Systems
Volume	19
Issue number	3
DOIs	https://doi.org/10.1109/TFUZZ.2011.2105880
State	Published - 1 Jun 2011

Keywords

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

Access to Document

10.1109/TFUZZ.2011.2105880

Fingerprint Dive into the research topics of 'Template-based shell clustering using a line-segment representation of data'. Together they form a unique fingerprint.

Cite this

@article{fb9067b37bad462c855d9391a8e6930f,

title = "Template-based shell clustering using a line-segment representation of data",

abstract = "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.",

keywords = "Line-segment approximation, line-segment matching, line-segment models, possibilistic c-means (PCMs), shell clustering, template matching, template-based clustering",

author = "Tsai-Pei Wang",

year = "2011",

month = jun,

day = "1",

doi = "10.1109/TFUZZ.2011.2105880",

language = "English",

volume = "19",

pages = "575--580",

journal = "IEEE Transactions on Fuzzy Systems",

issn = "1063-6706",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

number = "3",

}

TY - JOUR

T1 - Template-based shell clustering using a line-segment representation of data

AU - Wang, Tsai-Pei

PY - 2011/6/1

Y1 - 2011/6/1

N2 - 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.

AB - 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.

KW - Line-segment approximation

KW - line-segment matching

KW - line-segment models

KW - possibilistic c-means (PCMs)

KW - shell clustering

KW - template matching

KW - template-based clustering

UR - http://www.scopus.com/inward/record.url?scp=79957991315&partnerID=8YFLogxK

U2 - 10.1109/TFUZZ.2011.2105880

DO - 10.1109/TFUZZ.2011.2105880

M3 - Article

AN - SCOPUS:79957991315

VL - 19

SP - 575

EP - 580

JO - IEEE Transactions on Fuzzy Systems

JF - IEEE Transactions on Fuzzy Systems

SN - 1063-6706

IS - 3

M1 - 5686926

ER -

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

Abstract

Keywords

Access to Document

Other files and links

Cite this