Clustering algorithm in normalised cuts based image segmentation

Mei Yeen Choong; Wei Leong Khong; Renee Ka Yin Chin; Farrah Wong; Kenneth Tze Kin Teo

doi:10.1109/AMS.2013.32

Clustering algorithm in normalised cuts based image segmentation

Mei Yeen Choong, Wei Leong Khong, Renee Ka Yin Chin, Farrah Wong, Kenneth Tze Kin Teo

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Other

3 Citations (Scopus)

Abstract

Normalised cut method has been effectively used for image segmentation by representing an image as weighted graph in global view. It does segmentation via partitioning the graphs into sub-graphs. Clustering algorithm is implemented such that sub-graphs with common similarities are grouped together into one cluster and separates sub-graphs that are dissimilar into distinctive clusters. Clustered segments from the normalised cuts are then produced. As the clusters initialisation gives influence to the segmentation result, optimisation of the clustering algorithm is implemented to achieve better segmentation. With the approach applied in the normalised cuts based image segmentation, the constraint of using normalised cuts algorithm in image segmentation can be alleviated. In this paper, evaluation of the clustering algorithm with the normalised cuts image segmentation on images has been carried out and the effect of different image complexity towards normalised cuts segmentation process is presented.

Original language	English
Title of host publication	Proceedings - Asia Modelling Symposium 2013
Subtitle of host publication	7th Asia International Conference on Mathematical Modelling and Computer Simulation, AMS 2013
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	166-171
Number of pages	6
ISBN (Print)	9780769551012
DOIs	https://doi.org/10.1109/AMS.2013.32
Publication status	Published - 2013
Externally published	Yes
Event	Asia International Conference on Modelling and Simulation 2013 - Kuala Lumpur, Malaysia Duration: 23 Jul 2013 → 25 Jul 2013 Conference number: 7th https://ieeexplore.ieee.org/xpl/conhome/6663233/proceeding (Proceedings)

Conference

Conference	Asia International Conference on Modelling and Simulation 2013
Abbreviated title	AMS 2013
Country/Territory	Malaysia
City	Kuala Lumpur
Period	23/07/13 → 25/07/13
Internet address	https://ieeexplore.ieee.org/xpl/conhome/6663233/proceeding (Proceedings)

Keywords

fuzzy clustering
image segmentation
k-means clustering
normalised cut

Access to Document

10.1109/AMS.2013.32

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Choong, M. Y., Khong, W. L., Chin, R. K. Y., Wong, F., & Teo, K. T. K. (2013). Clustering algorithm in normalised cuts based image segmentation. In Proceedings - Asia Modelling Symposium 2013: 7th Asia International Conference on Mathematical Modelling and Computer Simulation, AMS 2013 (pp. 166-171). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/AMS.2013.32

@inproceedings{c16e6efc67fb425085ffc46eb6fd2ba1,

title = "Clustering algorithm in normalised cuts based image segmentation",

abstract = "Normalised cut method has been effectively used for image segmentation by representing an image as weighted graph in global view. It does segmentation via partitioning the graphs into sub-graphs. Clustering algorithm is implemented such that sub-graphs with common similarities are grouped together into one cluster and separates sub-graphs that are dissimilar into distinctive clusters. Clustered segments from the normalised cuts are then produced. As the clusters initialisation gives influence to the segmentation result, optimisation of the clustering algorithm is implemented to achieve better segmentation. With the approach applied in the normalised cuts based image segmentation, the constraint of using normalised cuts algorithm in image segmentation can be alleviated. In this paper, evaluation of the clustering algorithm with the normalised cuts image segmentation on images has been carried out and the effect of different image complexity towards normalised cuts segmentation process is presented.",

keywords = "fuzzy clustering, image segmentation, k-means clustering, normalised cut",

author = "Choong, {Mei Yeen} and Khong, {Wei Leong} and Chin, {Renee Ka Yin} and Farrah Wong and Teo, {Kenneth Tze Kin}",

year = "2013",

doi = "10.1109/AMS.2013.32",

language = "English",

isbn = "9780769551012",

pages = "166--171",

booktitle = "Proceedings - Asia Modelling Symposium 2013",

publisher = "IEEE, Institute of Electrical and Electronics Engineers",

address = "United States of America",

url = "https://ieeexplore.ieee.org/xpl/conhome/6663233/proceeding",

}

Choong, MY, Khong, WL, Chin, RKY, Wong, F & Teo, KTK 2013, Clustering algorithm in normalised cuts based image segmentation. in Proceedings - Asia Modelling Symposium 2013: 7th Asia International Conference on Mathematical Modelling and Computer Simulation, AMS 2013. IEEE, Institute of Electrical and Electronics Engineers, pp. 166-171, Asia International Conference on Modelling and Simulation 2013, Kuala Lumpur, Malaysia, 23/07/13. https://doi.org/10.1109/AMS.2013.32

Clustering algorithm in normalised cuts based image segmentation. / Choong, Mei Yeen; Khong, Wei Leong; Chin, Renee Ka Yin et al.
Proceedings - Asia Modelling Symposium 2013: 7th Asia International Conference on Mathematical Modelling and Computer Simulation, AMS 2013. IEEE, Institute of Electrical and Electronics Engineers, 2013. p. 166-171.

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Other

TY - GEN

T1 - Clustering algorithm in normalised cuts based image segmentation

AU - Choong, Mei Yeen

AU - Khong, Wei Leong

AU - Chin, Renee Ka Yin

AU - Wong, Farrah

AU - Teo, Kenneth Tze Kin

N1 - Conference code: 7th

PY - 2013

Y1 - 2013

N2 - Normalised cut method has been effectively used for image segmentation by representing an image as weighted graph in global view. It does segmentation via partitioning the graphs into sub-graphs. Clustering algorithm is implemented such that sub-graphs with common similarities are grouped together into one cluster and separates sub-graphs that are dissimilar into distinctive clusters. Clustered segments from the normalised cuts are then produced. As the clusters initialisation gives influence to the segmentation result, optimisation of the clustering algorithm is implemented to achieve better segmentation. With the approach applied in the normalised cuts based image segmentation, the constraint of using normalised cuts algorithm in image segmentation can be alleviated. In this paper, evaluation of the clustering algorithm with the normalised cuts image segmentation on images has been carried out and the effect of different image complexity towards normalised cuts segmentation process is presented.

AB - Normalised cut method has been effectively used for image segmentation by representing an image as weighted graph in global view. It does segmentation via partitioning the graphs into sub-graphs. Clustering algorithm is implemented such that sub-graphs with common similarities are grouped together into one cluster and separates sub-graphs that are dissimilar into distinctive clusters. Clustered segments from the normalised cuts are then produced. As the clusters initialisation gives influence to the segmentation result, optimisation of the clustering algorithm is implemented to achieve better segmentation. With the approach applied in the normalised cuts based image segmentation, the constraint of using normalised cuts algorithm in image segmentation can be alleviated. In this paper, evaluation of the clustering algorithm with the normalised cuts image segmentation on images has been carried out and the effect of different image complexity towards normalised cuts segmentation process is presented.

KW - fuzzy clustering

KW - image segmentation

KW - k-means clustering

KW - normalised cut

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BT - Proceedings - Asia Modelling Symposium 2013

PB - IEEE, Institute of Electrical and Electronics Engineers

T2 - Asia International Conference on Modelling and Simulation 2013

Y2 - 23 July 2013 through 25 July 2013

ER -

Clustering algorithm in normalised cuts based image segmentation

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Keywords

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