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.