Abstract: A similar class merging based FCM algorithm for image segmentation was proposed to solve the problems that the segmentation results of the traditional FCM based image segmentation algorithm are discrete in the spatial distribution and the object cannot be segmented accurately by the traditional FCM based method. Firstly, a global spatial similarity measure and a global intensity similarity measure were proposed and introduced into a novel distance metric to calculate the difference between the pixels and the cluster centers. Secondly, color histogram was used as a descriptor, and Bhattacharyya distance was used to calculate the similarity between any two classes. Finally, a maximal similarity based class merging strategy was used to obtain the final image segmentation results. The experimental results indicated that the proposed algorithm can obtain more accurate image segmentation results compared with FCM and KFCM methods.