A Gaussian mixture model-based clustering algorithm for image segmentation using dependable spatial constraints

Weiling Cai; Lei Lei; Ming Yang

doi:10.1109/CISP.2010.5647653

A Gaussian mixture model-based clustering algorithm for image segmentation using dependable spatial constraints

Weiling Cai^*, Lei Lei, Ming Yang

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

13 Citations (Scopus)

Abstract

In this paper, a Gaussian Mixture Model-based clustering algorithm using dependable spatial constraints is proposed for image segmentation. In order to enhance the segmentation performance, the proposed algortihm utilizes the consistence between the pixel and its local window to discriminate uncorrupted pixels from corrupted pixels. Then, using these uncorrupted pixels, the dependable spatial constraints are applied to influence the labeling of the pixel. In this way, the spatial information with high reliability is incorporated into the segmentation process, as a result, the segmentation accuracy is guaranteed to a great extent. The extensive segmentation experiments on both synthetic and real images demonstrate the effectiveness of the proposed algorithm.

Original language	English
Title of host publication	Proceedings - 2010 3rd International Congress on Image and Signal Processing, CISP 2010
Pages	1268-1272
Number of pages	5
DOIs	https://doi.org/10.1109/CISP.2010.5647653
Publication status	Published - 2010
Externally published	Yes
Event	2010 3rd International Congress on Image and Signal Processing, CISP 2010 - Yantai, China Duration: 16 Oct 2010 → 18 Oct 2010

Publication series

Name	Proceedings - 2010 3rd International Congress on Image and Signal Processing, CISP 2010
Volume	3

Conference

Conference	2010 3rd International Congress on Image and Signal Processing, CISP 2010
Country/Territory	China
City	Yantai
Period	16/10/10 → 18/10/10

Keywords

Clustering analysis
Gaussian mixture model
Image segmentation

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10.1109/CISP.2010.5647653

Cite this

Cai, W., Lei, L., & Yang, M. (2010). A Gaussian mixture model-based clustering algorithm for image segmentation using dependable spatial constraints. In Proceedings - 2010 3rd International Congress on Image and Signal Processing, CISP 2010 (pp. 1268-1272). Article 5647653 (Proceedings - 2010 3rd International Congress on Image and Signal Processing, CISP 2010; Vol. 3). https://doi.org/10.1109/CISP.2010.5647653

@inproceedings{cc1d00bd2a9c44ada4e50982bb975ef1,

title = "A Gaussian mixture model-based clustering algorithm for image segmentation using dependable spatial constraints",

abstract = "In this paper, a Gaussian Mixture Model-based clustering algorithm using dependable spatial constraints is proposed for image segmentation. In order to enhance the segmentation performance, the proposed algortihm utilizes the consistence between the pixel and its local window to discriminate uncorrupted pixels from corrupted pixels. Then, using these uncorrupted pixels, the dependable spatial constraints are applied to influence the labeling of the pixel. In this way, the spatial information with high reliability is incorporated into the segmentation process, as a result, the segmentation accuracy is guaranteed to a great extent. The extensive segmentation experiments on both synthetic and real images demonstrate the effectiveness of the proposed algorithm.",

keywords = "Clustering analysis, Gaussian mixture model, Image segmentation",

author = "Weiling Cai and Lei Lei and Ming Yang",

year = "2010",

doi = "10.1109/CISP.2010.5647653",

language = "English",

isbn = "9781424465149",

series = "Proceedings - 2010 3rd International Congress on Image and Signal Processing, CISP 2010",

pages = "1268--1272",

booktitle = "Proceedings - 2010 3rd International Congress on Image and Signal Processing, CISP 2010",

note = "2010 3rd International Congress on Image and Signal Processing, CISP 2010 ; Conference date: 16-10-2010 Through 18-10-2010",

}

Cai, W, Lei, L & Yang, M 2010, A Gaussian mixture model-based clustering algorithm for image segmentation using dependable spatial constraints. in Proceedings - 2010 3rd International Congress on Image and Signal Processing, CISP 2010., 5647653, Proceedings - 2010 3rd International Congress on Image and Signal Processing, CISP 2010, vol. 3, pp. 1268-1272, 2010 3rd International Congress on Image and Signal Processing, CISP 2010, Yantai, China, 16/10/10. https://doi.org/10.1109/CISP.2010.5647653

A Gaussian mixture model-based clustering algorithm for image segmentation using dependable spatial constraints. / Cai, Weiling; Lei, Lei; Yang, Ming.
Proceedings - 2010 3rd International Congress on Image and Signal Processing, CISP 2010. 2010. p. 1268-1272 5647653 (Proceedings - 2010 3rd International Congress on Image and Signal Processing, CISP 2010; Vol. 3).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A Gaussian mixture model-based clustering algorithm for image segmentation using dependable spatial constraints

AU - Cai, Weiling

AU - Lei, Lei

AU - Yang, Ming

PY - 2010

Y1 - 2010

N2 - In this paper, a Gaussian Mixture Model-based clustering algorithm using dependable spatial constraints is proposed for image segmentation. In order to enhance the segmentation performance, the proposed algortihm utilizes the consistence between the pixel and its local window to discriminate uncorrupted pixels from corrupted pixels. Then, using these uncorrupted pixels, the dependable spatial constraints are applied to influence the labeling of the pixel. In this way, the spatial information with high reliability is incorporated into the segmentation process, as a result, the segmentation accuracy is guaranteed to a great extent. The extensive segmentation experiments on both synthetic and real images demonstrate the effectiveness of the proposed algorithm.

AB - In this paper, a Gaussian Mixture Model-based clustering algorithm using dependable spatial constraints is proposed for image segmentation. In order to enhance the segmentation performance, the proposed algortihm utilizes the consistence between the pixel and its local window to discriminate uncorrupted pixels from corrupted pixels. Then, using these uncorrupted pixels, the dependable spatial constraints are applied to influence the labeling of the pixel. In this way, the spatial information with high reliability is incorporated into the segmentation process, as a result, the segmentation accuracy is guaranteed to a great extent. The extensive segmentation experiments on both synthetic and real images demonstrate the effectiveness of the proposed algorithm.

KW - Clustering analysis

KW - Gaussian mixture model

KW - Image segmentation

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U2 - 10.1109/CISP.2010.5647653

DO - 10.1109/CISP.2010.5647653

M3 - Conference contribution

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SN - 9781424465149

T3 - Proceedings - 2010 3rd International Congress on Image and Signal Processing, CISP 2010

SP - 1268

EP - 1272

BT - Proceedings - 2010 3rd International Congress on Image and Signal Processing, CISP 2010

T2 - 2010 3rd International Congress on Image and Signal Processing, CISP 2010

Y2 - 16 October 2010 through 18 October 2010

ER -

Cai W, Lei L, Yang M. A Gaussian mixture model-based clustering algorithm for image segmentation using dependable spatial constraints. In Proceedings - 2010 3rd International Congress on Image and Signal Processing, CISP 2010. 2010. p. 1268-1272. 5647653. (Proceedings - 2010 3rd International Congress on Image and Signal Processing, CISP 2010). doi: 10.1109/CISP.2010.5647653

A Gaussian mixture model-based clustering algorithm for image segmentation using dependable spatial constraints

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