Earticle

Color image segmentation algorithms are proposed based on granular computing clustering (GrCC). Firstly, the atomic hyperspherical granule is represented as the vector including the RGB value of pixel of color image and radii 0. Secondly, the union operator of two hyperspherical granules is designed to obtain the larger hyperspherical granule compared with these two hyperspherical granules. Thirdly, the granular computing clustering is developed by the union operator and the user-defined granularity threshold . Global Consistency Error (GCE), Variation of Information (VI), Rand Index (RI), and Loss Entropy (ΔEn) are used to evaluate the segmentations. Segmentations of the color images selected from internet and BSD300 show that segmentations by GrCC speed up the segmentation process and achieve the better segmentation performance compared with Kmeans and FCM segmentations.