Image segmentation is typically used to locate objects and boundaries in images. The result of image segmentation is a set of segments that collectively cover the entire image, or a set of contours extracted from the image. K-means clustering is a method of cluster analysis which aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean. The K-Means algorithm is used to find natural clusters within given data based upon varying input parameters. The method tries to develop k-means algorithm to obtain high performance and efficiency. Clusters can be formed for images based on pixel intensity, color, texture, location, or some combination of these. K-Means algorithms typically converge to a solution very quickly as opposed to other algorithms.
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