Finding Arbitrary Shaped Clusters and Color Image Segmentation

One of the most famous approaches for the segmentation of color images is finding clusters in the color space. Shapes of these clusters are often complex and the time complexity of the existing algorithms for finding clusters of different shapes is usually high. In this paper, a novel clustering algorithm is proposed and used for the image segmentation purpose. This algorithm distinguishes clusters of different shapes using a two-stage clustering approach in a reasonable time. In the first stage, the mean-shift clustering algorithm is used and the data points are grouped into some sub-clusters. In the second stage, connections between sub-clusters are established according to a dissimilarity measure and final clusters are formed. Experimental results show the ability of the proposed algorithm for finding clusters of arbitrary shapes in synthetic datasets and also for the segmentation of color images.