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Journal of Electronic Imaging

Image segmentation using fuzzy rules derived from K-means clusters
Author(s): Zheru Chi; Hong Yan
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Paper Abstract

Image segmentation is one of the most important steps in computerized systems for analyzing geographic map images. We present a segmentation technique, based on fuzzy rules derived from the K-means clusters, that is aimed at achieving human-like performance. In this technique, the K-means clustering algorithm is first used to obtain mixed-class clusters of training examples, whose centers and variances are then used to determine membership functions. Based on the derived membership functions, fuzzy rules are learned from the K-means cluster centers. In the map image segmentation, we make use of three features, difference intensity, standard deviation, and a measure of the local contrast, to classify each pixel to the foreground, which consists of character and fine patterns, and to the background. A centroid defuzzification algorithm is adopted in the classification step. Experimental results on a database of 22 gray-scale map images show that the technique achieves good and reliable results, and is compared favorably with an adaptive thresholding method. By using K-means clustering, we can build a segmentation system of fewer rules that achieves a segmentation quality similar to that of using the uniformly distributed triangular membership functions with the fuzzy rules learned from all the training examples.

Paper Details

Date Published: 1 April 1995
PDF: 8 pages
J. Electron. Imaging. 4(2) doi: 10.1117/12.203077
Published in: Journal of Electronic Imaging Volume 4, Issue 2
Show Author Affiliations
Zheru Chi, Univ. of Sydney (Australia)
Hong Yan, Univ. of Sydney (Australia)

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