Aiming at false segmentation of small regions and high computational complexity in traditional color image segmentation algorithm, a hierarchical method of color image segmentation based on rough set and HIS (Hue-Saturation-Intensity) space was proposed. Firstly, for the reason that the singularities in HSI space are the achromatic pixels in RGB space, the achromatic regions of RGB space were segmented and labeled in order to remove the singularities from the original image. Secondly, the original image was converted from RGB space to HSI space. In intensity component, in view of spatial neighbor information and regional distribution difference, the original histogram was weighted by homogeneity function with changing thresholds and gradience. The weighted and original histograms were respectively used as the upper and lower approximation sets of rough set. The new roughness function was defined and applied to image segmentation. Then the different regions obtained in the previous stage were segmented according to the histogram in hue component. Finally, the homogeneous regions were merged in RGB space in order to avoid over-segmentation. Compared with the method based on rough set proposed by Mushrif etc. (MUSHRIF M M, RAY A K. Color image segmentation: rough-set theoretic approach. Pattern Recognition Letters, 2008, 29(4): 483-493), the proposed method can segment small regions easily, avoid the false segmentation caused by the correlation between RGB color components, and the executing speed is 5-8 times faster. The experimental results show the proposed method yields better segmentation, and it is efficient and robust to noise.