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An image needs to be segmented before object-based coding can be performed. This paper proposes a practical method of doing this by region integration. Clustering based on the K-means algorithm is widely used, but this tends to segment the image into too many small regions. Further, since the shape of initial clusters has a lasting influence, region segmentation sometimes takes place in areas other than the original contour of an object in the image. To overcome these problems, we propose a threestep region integration based on the K-means algorithm. We applied this new method to several different natural images. The results show that we can extract the contours of regions from different images even with the same parameters, and even after varying the number of initial clusters. © 1998 Scripta Technica, Syst Comp Jpn, 29(14): 6880, 1998

Image segmentation by region integration using initial dependence of the K-means algorithm