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Mean shift algorithm is a nonparametric statistical method for seeking the main modes of a point sample distribution. This process achieves a high quality, discontinuity preserving spatial filtering. For the segmentation task, the convergence points sufficiently close in the joint domain are fused to obtain the homogeneous regions in the image. However, a color image is segmented differently according to the inputted spatial parameter or range parameter and the image is broken into many small regions in case of the small parameter. In this paper, to improve this demerit, we propose the method that groups similar regions using region merging method for oversegmented images. The proposed method converts an over-segmented image in RGB color space into in HSI color space and merges similar regions by hue information. Here, to preserve edge information, the proposed method use by region merging constraints to decide whether regions is merged or not. After then, we merge the regions in RGB color space for nonprocessed regions in HSI color space. Experimental results show the proposed method is superior to conventional methods in region's segmentation results.