Computer Science and Information Systems 2012 Volume 9, Issue 4, Pages: 1679-1696
https://doi.org/10.2298/CSIS120126052S
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Application of grid-based k-means clustering algorithm for optimal image processing
Shi Tingna (School of Electrical Engineering and Automation, Tianjin University, Tianjin, China)
Wang Penglong (School of Electrical Engineering and Automation, Tianjin University, Tianjin, China)
Wang Jeenshing (Department of Electrical Engineering, National Cheng Kung University, Tainan, Taiwan)
Yue Shihong (School of Electrical Engineering and Automation, Tianjin University, Tianjin, China)
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.
Keywords: electrical tomography, number of pixels, image ronconstruction