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This paper proposes a heuristically optimized version of Improved Mountain Clustering (IMC) Technique referred to as IMC-2. IMC-2 provides better quality clusters measured in terms of Global Silhouette and Separation indices as measures of information. The IMC-2 based color segmentation approach has been applied to various categories of images including face, stripes and grayscale images and compared with some extensively used clustering techniques such as K-means and FCM. The color segmentation performance has been compared on widely used and accepted validation indices, Global Silhouette Index and Separation Index. The color segments or clusters obtained have been verified visually and validated quantitatively.