About the journal

• Home page
• Contact information
• Aims and scope
• Indexing information
• Editorial policies
• ComSIS consortium
• Journal boards
• Managing board

For authors

• Information for contributors
• Paper submission
• Article submission through OJS
• Copyright transfer form
• Download section

For readers

• Forthcoming articles
• Current issue
• Archive

For reviewers

• View and review submissions

News

• Journal's Facebook page
• Calls for special issues
• New issue notification

Application of Grid-based K-means Clustering Algorithm for Optimal Image Processing

Tingna Shi¹, Penglong Wang¹, Jeenshing Wangb² and Shihong Yue¹

1. School of Electrical Engineering and Automation, Tianjin University
   Tianjin, China
2. Department of Electrical Engineering, National Cheng Kung University
   Tainan 701, Taiwan
   Shyue1999@tju.edu.cn

Abstract

The effectiveness of K-means clustering algorithm for image segmentation has been proven in many studies, but is limited in the following problems: 1) the determination of a proper number of clusters. If the number of clusters is determined incorrectly, a good-quality segmented image cannot be guaranteed; 2) the poor typicality of clustering prototypes; and 3) the determination of an optimal number of pixels. The number of pixels plays an important role in any image processing, but so far there is no general and efficient method to determine the optimal number of pixels. In this paper, a grid-based K-means algorithm is proposed for image segmentation. The advantages of the proposed algorithm over the existing K-means algorithm have been validated by some benchmark datasets. In addition, we further analyze the basic characteristics of the algorithm and propose a general index based on maximizing grey differences between investigated objective grays and background grays. Without any additional condition, the proposed index is robust in identifying an optimal number of pixels. Our experiments have validated the effectiveness of the proposed index by the image results that are consistent with the visual perception of the datasets.

Key words

electrical tomography; number of pixels; image ronconstruction

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS120126052S

Publication information

Volume 9, Issue 4 (December 2012)
Special Issue on Recent Advances in Systems and Informatics
Year of Publication: 2012
ISSN: 2406-1018 (Online)
Publisher: ComSIS Consortium

Full text

Available in PDF
Portable Document Format

How to cite

Shi, T., Wang, P., Wangb, J., Yue, S.: Application of Grid-based K-means Clustering Algorithm for Optimal Image Processing. Computer Science and Information Systems, Vol. 9, No. 4, 1679-1696. (2012), https://doi.org/10.2298/CSIS120126052S