Image segmentation is considered one of the most difficult challenges in image processing. Recently many advanced applications have emerged in this field. Color images provide more information and more reliable in segmentation than grayscale images. In this paper, the color spaces RGB, YCbCr, XYZ, and HSV are compared using four different methods of image segmentation. These methods are k-means, Fuzzy C-means, Region growing, and Graph Cut. Themain objective of image segmentation is to simplify and change the image to something more meaningful and easier to analyze. In this study, we used single-color space components. In addition to this, we vote between the three components of every color space in the segmented image to get the best image segmentation result. Different RGB color images from Berkeley databases are used. The accuracy of the image segmentation is measured using the peak signal-to-noise ratio (PSNR) and mean square error (MSE). The experimental results show that the voting between color components achieved good segmentation accuracy.