Ensemble evaluation for image segmentation using a semi-supervised SVM

Sang Jun Lee; Sang Gyu Ryu; Yong Ju Jeon; Doo Chul Choi; Sang Woo Kim

doi:10.2316/P.2013.798-082

Ensemble evaluation for image segmentation using a semi-supervised SVM

Sang Jun Lee^*
, Sang Gyu Ryu
, Yong Ju Jeon
, Doo Chul Choi
, Sang Woo Kim

^*Corresponding author for this work

Division of Electronic Engineering

Pohang University of Science and Technology

Research output: Contribution to conference › Paper › peer-review

Abstract

A new unsupervised ensemble evaluation algorithm is proposed for image segmentation. A semi-supervised support vector machine is used to combine the existing unsupervised evaluators. We also proposed feature extraction and data selection procedures to enhance the overall performance. We experimentally demonstrated that our proposed algorithm is superior to existing segmentation evaluation measures.

Original language	English
Pages	349-355
Number of pages	7
DOIs	https://doi.org/10.2316/P.2013.798-082
State	Published - 2013
Event	IASTED International Conference on Signal Processing, Pattern Recognition and Applications, SPPRA 2013 - Innsbruck, Austria Duration: 2013.02.12 → 2013.02.14

Conference

Conference	IASTED International Conference on Signal Processing, Pattern Recognition and Applications, SPPRA 2013
Country/Territory	Austria
City	Innsbruck
Period	13.02.12 → 13.02.14

Keywords

Pattern recognition
Segmentation and representation
Segmentation evaluation
Support vector machine

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10.2316/P.2013.798-082

Cite this

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title = "Ensemble evaluation for image segmentation using a semi-supervised SVM",

abstract = "A new unsupervised ensemble evaluation algorithm is proposed for image segmentation. A semi-supervised support vector machine is used to combine the existing unsupervised evaluators. We also proposed feature extraction and data selection procedures to enhance the overall performance. We experimentally demonstrated that our proposed algorithm is superior to existing segmentation evaluation measures.",

keywords = "Pattern recognition, Segmentation and representation, Segmentation evaluation, Support vector machine",

author = "Lee, \{Sang Jun\} and Ryu, \{Sang Gyu\} and Jeon, \{Yong Ju\} and Choi, \{Doo Chul\} and Kim, \{Sang Woo\}",

year = "2013",

doi = "10.2316/P.2013.798-082",

language = "English",

pages = "349--355",

note = "IASTED International Conference on Signal Processing, Pattern Recognition and Applications, SPPRA 2013 ; Conference date: 12-02-2013 Through 14-02-2013",

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T1 - Ensemble evaluation for image segmentation using a semi-supervised SVM

AU - Lee, Sang Jun

AU - Ryu, Sang Gyu

AU - Jeon, Yong Ju

AU - Choi, Doo Chul

AU - Kim, Sang Woo

PY - 2013

Y1 - 2013

N2 - A new unsupervised ensemble evaluation algorithm is proposed for image segmentation. A semi-supervised support vector machine is used to combine the existing unsupervised evaluators. We also proposed feature extraction and data selection procedures to enhance the overall performance. We experimentally demonstrated that our proposed algorithm is superior to existing segmentation evaluation measures.

AB - A new unsupervised ensemble evaluation algorithm is proposed for image segmentation. A semi-supervised support vector machine is used to combine the existing unsupervised evaluators. We also proposed feature extraction and data selection procedures to enhance the overall performance. We experimentally demonstrated that our proposed algorithm is superior to existing segmentation evaluation measures.

KW - Pattern recognition

KW - Segmentation and representation

KW - Segmentation evaluation

KW - Support vector machine

UR - https://www.scopus.com/pages/publications/84876569659

U2 - 10.2316/P.2013.798-082

DO - 10.2316/P.2013.798-082

M3 - Paper

AN - SCOPUS:84876569659

SP - 349

EP - 355

T2 - IASTED International Conference on Signal Processing, Pattern Recognition and Applications, SPPRA 2013

Y2 - 12 February 2013 through 14 February 2013

ER -

Ensemble evaluation for image segmentation using a semi-supervised SVM

Abstract

Conference

Keywords

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