Towards a Hybrid Approach of Self-Organizing Map and Density-Based Spatial Clustering of Applications with Noise for Image Segmentation

Qi Chen; Kevin Kam Fung Yuen; Chun Guan

doi:10.1109/DeSE.2017.24

Towards a Hybrid Approach of Self-Organizing Map and Density-Based Spatial Clustering of Applications with Noise for Image Segmentation

Qi Chen
, Kevin Kam Fung Yuen
, Chun Guan

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

4 Citations (Scopus)

Abstract

Image segmentation is the process to divide a digital image into a number of regions for further usage. Color images can be segmented by applying density-based clustering methods, e.g. Density-Based Spatial Clustering of Applications with Noise (DBSCAN), which is used to identify the arbitrary shaped clusters. The drawback of DBSCAN is the high computational complexity whilst the size of image input is normally very large. Self-Organizing Map (SOM) is a dimensionality reduction method which can be applied to reduce the dimensions of image processing tasks. This paper proposes a hybrid method of SOM and DBSCAN (SOM-DBSCAN) for image segmentation. To evaluating the usability of the proposed SOM-DBSCAN method, four images are used to benchmark image segmentation.

Original language	English
Title of host publication	Proceedings - 2017 10th International Conference on Developments in eSystems Engineering, DeSE 2017
Editors	Hani Hamdan, Dhiya Al-Jumeily, Abir Hussain, Hissam Tawfik, Jade Hind
Place of Publication	Piscataway NJ USA
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	238-241
Number of pages	4
ISBN (Electronic)	9781538617212
DOIs	https://doi.org/10.1109/DeSE.2017.24
Publication status	Published - 2 Jul 2017
Externally published	Yes
Event	International Conference on Developments in eSystems Engineering, DeSE 2017 - Paris, France Duration: 14 Jun 2017 → 16 Jun 2017 Conference number: 10th

Publication series

Name	Proceedings - International Conference on Developments in eSystems Engineering, DeSE
ISSN (Print)	2161-1343

Conference

Conference	International Conference on Developments in eSystems Engineering, DeSE 2017
Abbreviated title	DeSE 2017
Country/Territory	France
City	Paris
Period	14/06/17 → 16/06/17

Keywords

Computer Vision
DBSCAN
Image Segmentation
SOM

Access to Document

10.1109/DeSE.2017.24

Cite this

Chen, Q., Yuen, K. K. F., & Guan, C. (2017). Towards a Hybrid Approach of Self-Organizing Map and Density-Based Spatial Clustering of Applications with Noise for Image Segmentation. In H. Hamdan, D. Al-Jumeily, A. Hussain, H. Tawfik, & J. Hind (Eds.), Proceedings - 2017 10th International Conference on Developments in eSystems Engineering, DeSE 2017 (pp. 238-241). Article 8285827 (Proceedings - International Conference on Developments in eSystems Engineering, DeSE). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/DeSE.2017.24

Chen, Qi ; Yuen, Kevin Kam Fung ; Guan, Chun. / Towards a Hybrid Approach of Self-Organizing Map and Density-Based Spatial Clustering of Applications with Noise for Image Segmentation. Proceedings - 2017 10th International Conference on Developments in eSystems Engineering, DeSE 2017. editor / Hani Hamdan ; Dhiya Al-Jumeily ; Abir Hussain ; Hissam Tawfik ; Jade Hind. Piscataway NJ USA : IEEE, Institute of Electrical and Electronics Engineers, 2017. pp. 238-241 (Proceedings - International Conference on Developments in eSystems Engineering, DeSE).

@inproceedings{8474d55192cb4a61ab60fc8d5800639e,

title = "Towards a Hybrid Approach of Self-Organizing Map 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 usage. Color images can be segmented by applying density-based clustering methods, e.g. Density-Based Spatial Clustering of Applications with Noise (DBSCAN), which is used to identify the arbitrary shaped clusters. The drawback of DBSCAN is the high computational complexity whilst the size of image input is normally very large. Self-Organizing Map (SOM) is a dimensionality reduction method which can be applied to reduce the dimensions of image processing tasks. This paper proposes a hybrid method of SOM and DBSCAN (SOM-DBSCAN) for image segmentation. To evaluating the usability of the proposed SOM-DBSCAN method, four images are used to benchmark image segmentation.",

keywords = "Computer Vision, DBSCAN, Image Segmentation, SOM",

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

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; International Conference on Developments in eSystems Engineering, DeSE 2017, DeSE 2017 ; Conference date: 14-06-2017 Through 16-06-2017",

year = "2017",

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doi = "10.1109/DeSE.2017.24",

language = "English",

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pages = "238--241",

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Chen, Q, Yuen, KKF & Guan, C 2017, Towards a Hybrid Approach of Self-Organizing Map and Density-Based Spatial Clustering of Applications with Noise for Image Segmentation. in H Hamdan, D Al-Jumeily, A Hussain, H Tawfik & J Hind (eds), Proceedings - 2017 10th International Conference on Developments in eSystems Engineering, DeSE 2017., 8285827, Proceedings - International Conference on Developments in eSystems Engineering, DeSE, IEEE, Institute of Electrical and Electronics Engineers, Piscataway NJ USA, pp. 238-241, International Conference on Developments in eSystems Engineering, DeSE 2017, Paris, France, 14/06/17. https://doi.org/10.1109/DeSE.2017.24

Towards a Hybrid Approach of Self-Organizing Map and Density-Based Spatial Clustering of Applications with Noise for Image Segmentation. / Chen, Qi; Yuen, Kevin Kam Fung; Guan, Chun.
Proceedings - 2017 10th International Conference on Developments in eSystems Engineering, DeSE 2017. ed. / Hani Hamdan; Dhiya Al-Jumeily; Abir Hussain; Hissam Tawfik; Jade Hind. Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers, 2017. p. 238-241 8285827 (Proceedings - International Conference on Developments in eSystems Engineering, DeSE).

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

TY - GEN

T1 - Towards a Hybrid Approach of Self-Organizing Map and Density-Based Spatial Clustering of Applications with Noise for Image Segmentation

AU - Chen, Qi

AU - Yuen, Kevin Kam Fung

AU - Guan, Chun

N1 - Conference code: 10th

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 usage. Color images can be segmented by applying density-based clustering methods, e.g. Density-Based Spatial Clustering of Applications with Noise (DBSCAN), which is used to identify the arbitrary shaped clusters. The drawback of DBSCAN is the high computational complexity whilst the size of image input is normally very large. Self-Organizing Map (SOM) is a dimensionality reduction method which can be applied to reduce the dimensions of image processing tasks. This paper proposes a hybrid method of SOM and DBSCAN (SOM-DBSCAN) for image segmentation. To evaluating the usability of the proposed SOM-DBSCAN method, four images are used to benchmark image segmentation.

AB - Image segmentation is the process to divide a digital image into a number of regions for further usage. Color images can be segmented by applying density-based clustering methods, e.g. Density-Based Spatial Clustering of Applications with Noise (DBSCAN), which is used to identify the arbitrary shaped clusters. The drawback of DBSCAN is the high computational complexity whilst the size of image input is normally very large. Self-Organizing Map (SOM) is a dimensionality reduction method which can be applied to reduce the dimensions of image processing tasks. This paper proposes a hybrid method of SOM and DBSCAN (SOM-DBSCAN) for image segmentation. To evaluating the usability of the proposed SOM-DBSCAN method, four images are used to benchmark image segmentation.

KW - Computer Vision

KW - DBSCAN

KW - Image Segmentation

KW - SOM

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M3 - Conference Paper

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T3 - Proceedings - International Conference on Developments in eSystems Engineering, DeSE

SP - 238

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BT - Proceedings - 2017 10th International Conference on Developments in eSystems Engineering, DeSE 2017

A2 - Hamdan, Hani

A2 - Al-Jumeily, Dhiya

A2 - Hussain, Abir

A2 - Tawfik, Hissam

A2 - Hind, Jade

PB - IEEE, Institute of Electrical and Electronics Engineers

CY - Piscataway NJ USA

T2 - International Conference on Developments in eSystems Engineering, DeSE 2017

Y2 - 14 June 2017 through 16 June 2017

ER -

Chen Q, Yuen KKF, Guan C. Towards a Hybrid Approach of Self-Organizing Map and Density-Based Spatial Clustering of Applications with Noise for Image Segmentation. In Hamdan H, Al-Jumeily D, Hussain A, Tawfik H, Hind J, editors, Proceedings - 2017 10th International Conference on Developments in eSystems Engineering, DeSE 2017. Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers. 2017. p. 238-241. 8285827. (Proceedings - International Conference on Developments in eSystems Engineering, DeSE). doi: 10.1109/DeSE.2017.24

Towards a Hybrid Approach of Self-Organizing Map and Density-Based Spatial Clustering of Applications with Noise for Image Segmentation

Abstract

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Keywords

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