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Optical Engineering

Segmentation of color images based on the gravitational clustering concept
Author(s): Andrew H. S. Lai; H. C. Yung
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Paper Abstract

A new clustering algorithm derived from the Markovian model of the gravitational clustering concept is proposed that works in the RGB measurement space for color image. To enable the model to be applicable in image segmentation, the new algorithm imposes a clustering constraint at each clustering iteration to control and determine the formation of multiple clusters. Using such constraint to limit the attraction between clusters, a termination condition can be easily defined. The new clustering algorithm is evaluated objectively and subjectively on three different images against the K-means clustering algorithm, the recursive histogram clustering algorithm for color (also known as the multi-spectral thresholding), the Hedley-Yan algorithm, and the widely used seedbased region growing algorithm. From the evaluation, it is observed that the new algorithm exhibits the following characteristics: (1) its objective measurement figures are comparable with the best in this group of segmentation algorithms; (2) it generates smoother region boundaries; (3) the segmented boundaries align closely with the original boundaries; and (4) it forms a meaningful number of segmented regions.

Paper Details

Date Published: 1 March 1998
PDF: 12 pages
Opt. Eng. 37(3) doi: 10.1117/1.601932
Published in: Optical Engineering Volume 37, Issue 3
Show Author Affiliations
Andrew H. S. Lai, Univ. of Hong Kong (Hong Kong)
H. C. Yung, Univ of Hong Kong (China)

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