Towards a Hybrid Approach of K-Means and Density-Based Spatial Clustering of Applications with Noise for Image Segmentation

Chun Guan; Kevin Kam Fung Yuen; Qi Chen

doi:10.1109/iThings-GreenCom-CPSCom-SmartData.2017.65

Towards a Hybrid Approach of K-Means and Density-Based Spatial Clustering of Applications with Noise for Image Segmentation

Chun Guan
, Kevin Kam Fung Yuen
, Qi Chen

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

11 Citations (Scopus)

Abstract

Image segmentation is the process to divide a digital image into a number of regions for further analysis in the area of computer vision. Color images can be segmented by applying various clustering algorithms such as DBSCAN, which can identify the arbitrary shaped clusters. The drawback of DBSCAN is the high computational complexity whilst the sizes of image datasets are normally very large. This paper proposes a hybrid method of K-means and DBSCAN (Kmeans-DBSCAN) for image segmentation. K-means is the common partition-based clustering approach to reduce the size of image dataset. Four benchmarking image segmentation cases are used for evaluating the usability of proposed Kmeans-DBSCAN method.

Original language	English
Title of host publication	Proceedings - 2017 IEEE International Conference on Internet of Things, IEEE Green Computing and Communications, IEEE Cyber, Physical and Social Computing, IEEE Smart Data, iThings-GreenCom-CPSCom-SmartData 2017
Editors	Yulei Wu, Geyong Min, Nektarios Georgalas, Ahmed Al-Dubi, Xiaolong Jin, Laurence T. Yang
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	396-399
Number of pages	4
ISBN (Electronic)	9781538630655
DOIs	https://doi.org/10.1109/iThings-GreenCom-CPSCom-SmartData.2017.65
Publication status	Published - 2 Jul 2017
Event	Joint 10th IEEE International Conference on Internet of Things, iThings 2017, 13th IEEE International Conference on Green Computing and Communications, GreenCom 2017, 10th IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2017 and the 3rd IEEE International Conference on Smart Data, Smart Data 2017 - Exeter, United Kingdom Duration: 21 Jun 2017 → 23 Jun 2017

Publication series

Name	Proceedings - 2017 IEEE International Conference on Internet of Things, IEEE Green Computing and Communications, IEEE Cyber, Physical and Social Computing, IEEE Smart Data, iThings-GreenCom-CPSCom-SmartData 2017
Volume	2018-January

Conference

Conference	Joint 10th IEEE International Conference on Internet of Things, iThings 2017, 13th IEEE International Conference on Green Computing and Communications, GreenCom 2017, 10th IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2017 and the 3rd IEEE International Conference on Smart Data, Smart Data 2017
Country/Territory	United Kingdom
City	Exeter
Period	21/06/17 → 23/06/17

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Clustering Analysis
Computer Vision
DBSCAN
Image Segmentation
K-means

Access to Document

10.1109/iThings-GreenCom-CPSCom-SmartData.2017.65

Cite this

Guan, C., Yuen, K. K. F., & Chen, Q. (2017). Towards a Hybrid Approach of K-Means and Density-Based Spatial Clustering of Applications with Noise for Image Segmentation. In Y. Wu, G. Min, N. Georgalas, A. Al-Dubi, X. Jin, & L. T. Yang (Eds.), Proceedings - 2017 IEEE International Conference on Internet of Things, IEEE Green Computing and Communications, IEEE Cyber, Physical and Social Computing, IEEE Smart Data, iThings-GreenCom-CPSCom-SmartData 2017 (pp. 396-399). (Proceedings - 2017 IEEE International Conference on Internet of Things, IEEE Green Computing and Communications, IEEE Cyber, Physical and Social Computing, IEEE Smart Data, iThings-GreenCom-CPSCom-SmartData 2017; Vol. 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/iThings-GreenCom-CPSCom-SmartData.2017.65

Guan, Chun ; Yuen, Kevin Kam Fung ; Chen, Qi. / Towards a Hybrid Approach of K-Means and Density-Based Spatial Clustering of Applications with Noise for Image Segmentation. Proceedings - 2017 IEEE International Conference on Internet of Things, IEEE Green Computing and Communications, IEEE Cyber, Physical and Social Computing, IEEE Smart Data, iThings-GreenCom-CPSCom-SmartData 2017. editor / Yulei Wu ; Geyong Min ; Nektarios Georgalas ; Ahmed Al-Dubi ; Xiaolong Jin ; Laurence T. Yang. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 396-399 (Proceedings - 2017 IEEE International Conference on Internet of Things, IEEE Green Computing and Communications, IEEE Cyber, Physical and Social Computing, IEEE Smart Data, iThings-GreenCom-CPSCom-SmartData 2017).

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title = "Towards a Hybrid Approach of K-Means and Density-Based Spatial Clustering of Applications with Noise for Image Segmentation",

abstract = "Image segmentation is the process to divide a digital image into a number of regions for further analysis in the area of computer vision. Color images can be segmented by applying various clustering algorithms such as DBSCAN, which can identify the arbitrary shaped clusters. The drawback of DBSCAN is the high computational complexity whilst the sizes of image datasets are normally very large. This paper proposes a hybrid method of K-means and DBSCAN (Kmeans-DBSCAN) for image segmentation. K-means is the common partition-based clustering approach to reduce the size of image dataset. Four benchmarking image segmentation cases are used for evaluating the usability of proposed Kmeans-DBSCAN method.",

keywords = "Clustering Analysis, Computer Vision, DBSCAN, Image Segmentation, K-means",

author = "Chun Guan and Yuen, \{Kevin Kam Fung\} and Qi Chen",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; Joint 10th IEEE International Conference on Internet of Things, iThings 2017, 13th IEEE International Conference on Green Computing and Communications, GreenCom 2017, 10th IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2017 and the 3rd IEEE International Conference on Smart Data, Smart Data 2017 ; Conference date: 21-06-2017 Through 23-06-2017",

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editor = "Yulei Wu and Geyong Min and Nektarios Georgalas and Ahmed Al-Dubi and Xiaolong Jin and Yang, \{Laurence T.\}",

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Guan, C, Yuen, KKF & Chen, Q 2017, Towards a Hybrid Approach of K-Means and Density-Based Spatial Clustering of Applications with Noise for Image Segmentation. in Y Wu, G Min, N Georgalas, A Al-Dubi, X Jin & LT Yang (eds), Proceedings - 2017 IEEE International Conference on Internet of Things, IEEE Green Computing and Communications, IEEE Cyber, Physical and Social Computing, IEEE Smart Data, iThings-GreenCom-CPSCom-SmartData 2017. Proceedings - 2017 IEEE International Conference on Internet of Things, IEEE Green Computing and Communications, IEEE Cyber, Physical and Social Computing, IEEE Smart Data, iThings-GreenCom-CPSCom-SmartData 2017, vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 396-399, Joint 10th IEEE International Conference on Internet of Things, iThings 2017, 13th IEEE International Conference on Green Computing and Communications, GreenCom 2017, 10th IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2017 and the 3rd IEEE International Conference on Smart Data, Smart Data 2017, Exeter, United Kingdom, 21/06/17. https://doi.org/10.1109/iThings-GreenCom-CPSCom-SmartData.2017.65

Towards a Hybrid Approach of K-Means and Density-Based Spatial Clustering of Applications with Noise for Image Segmentation. / Guan, Chun; Yuen, Kevin Kam Fung; Chen, Qi.
Proceedings - 2017 IEEE International Conference on Internet of Things, IEEE Green Computing and Communications, IEEE Cyber, Physical and Social Computing, IEEE Smart Data, iThings-GreenCom-CPSCom-SmartData 2017. ed. / Yulei Wu; Geyong Min; Nektarios Georgalas; Ahmed Al-Dubi; Xiaolong Jin; Laurence T. Yang. Institute of Electrical and Electronics Engineers Inc., 2017. p. 396-399 (Proceedings - 2017 IEEE International Conference on Internet of Things, IEEE Green Computing and Communications, IEEE Cyber, Physical and Social Computing, IEEE Smart Data, iThings-GreenCom-CPSCom-SmartData 2017; Vol. 2018-January).

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

TY - GEN

T1 - Towards a Hybrid Approach of K-Means and Density-Based Spatial Clustering of Applications with Noise for Image Segmentation

AU - Guan, Chun

AU - Yuen, Kevin Kam Fung

AU - Chen, Qi

PY - 2017/7/2

Y1 - 2017/7/2

N2 - Image segmentation is the process to divide a digital image into a number of regions for further analysis in the area of computer vision. Color images can be segmented by applying various clustering algorithms such as DBSCAN, which can identify the arbitrary shaped clusters. The drawback of DBSCAN is the high computational complexity whilst the sizes of image datasets are normally very large. This paper proposes a hybrid method of K-means and DBSCAN (Kmeans-DBSCAN) for image segmentation. K-means is the common partition-based clustering approach to reduce the size of image dataset. Four benchmarking image segmentation cases are used for evaluating the usability of proposed Kmeans-DBSCAN method.

AB - Image segmentation is the process to divide a digital image into a number of regions for further analysis in the area of computer vision. Color images can be segmented by applying various clustering algorithms such as DBSCAN, which can identify the arbitrary shaped clusters. The drawback of DBSCAN is the high computational complexity whilst the sizes of image datasets are normally very large. This paper proposes a hybrid method of K-means and DBSCAN (Kmeans-DBSCAN) for image segmentation. K-means is the common partition-based clustering approach to reduce the size of image dataset. Four benchmarking image segmentation cases are used for evaluating the usability of proposed Kmeans-DBSCAN method.

KW - Clustering Analysis

KW - Computer Vision

KW - DBSCAN

KW - Image Segmentation

KW - K-means

UR - https://www.scopus.com/pages/publications/85047426198

U2 - 10.1109/iThings-GreenCom-CPSCom-SmartData.2017.65

DO - 10.1109/iThings-GreenCom-CPSCom-SmartData.2017.65

M3 - Conference Proceeding

AN - SCOPUS:85047426198

T3 - Proceedings - 2017 IEEE International Conference on Internet of Things, IEEE Green Computing and Communications, IEEE Cyber, Physical and Social Computing, IEEE Smart Data, iThings-GreenCom-CPSCom-SmartData 2017

SP - 396

EP - 399

BT - Proceedings - 2017 IEEE International Conference on Internet of Things, IEEE Green Computing and Communications, IEEE Cyber, Physical and Social Computing, IEEE Smart Data, iThings-GreenCom-CPSCom-SmartData 2017

A2 - Wu, Yulei

A2 - Min, Geyong

A2 - Georgalas, Nektarios

A2 - Al-Dubi, Ahmed

A2 - Jin, Xiaolong

A2 - Yang, Laurence T.

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - Joint 10th IEEE International Conference on Internet of Things, iThings 2017, 13th IEEE International Conference on Green Computing and Communications, GreenCom 2017, 10th IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2017 and the 3rd IEEE International Conference on Smart Data, Smart Data 2017

Y2 - 21 June 2017 through 23 June 2017

ER -

Guan C, Yuen KKF, Chen Q. Towards a Hybrid Approach of K-Means and Density-Based Spatial Clustering of Applications with Noise for Image Segmentation. In Wu Y, Min G, Georgalas N, Al-Dubi A, Jin X, Yang LT, editors, Proceedings - 2017 IEEE International Conference on Internet of Things, IEEE Green Computing and Communications, IEEE Cyber, Physical and Social Computing, IEEE Smart Data, iThings-GreenCom-CPSCom-SmartData 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 396-399. (Proceedings - 2017 IEEE International Conference on Internet of Things, IEEE Green Computing and Communications, IEEE Cyber, Physical and Social Computing, IEEE Smart Data, iThings-GreenCom-CPSCom-SmartData 2017). doi: 10.1109/iThings-GreenCom-CPSCom-SmartData.2017.65

Towards a Hybrid Approach of K-Means and Density-Based Spatial Clustering of Applications with Noise for Image Segmentation

Abstract

Publication series

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UN SDGs

Keywords

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