IMAGE SEGMENTATION USING FUZZY RULES DERIVED FROM K-MEANS CLUSTERS

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, th at is aimed at achieving human-like performance, In this technique, th e K-means clustering algorithm is first used to obtain mixed-class clu sters of training examples, whose centers and variances are then used to determine membership functions, Based on the derived membership fun ctions, fuzzy rules are learned from the K-means cluster centers. In t he map image segmentation, we make use of three features, difference i ntensity, standard deviation, and a measure of the local contrast, to classify each pixel to the foreground which consists of character and line patterns, and to the background. A centroid defuzzification algor ithm 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 segmen tation system of fewer rules that achieves a segmentation quality simi lar to that of using the uniformly distributed triangular membership f unctions with the fuzzy rules learned from all the training examples.