  Color image segmentation is a very emerging research topic in the area of
color image analysis and pattern recognition. Many state-of-the-art algorithms
have been developed for this purpose. But, often the segmentation results of
these algorithms seem to be suffering from miss-classifications and
over-segmentation. The reasons behind these are the degradation of image
quality during the acquisition, transmission and color space conversion. So,
here arises the need of an efficient image enhancement technique which can
remove the redundant pixels or noises from the color image before proceeding
for final segmentation. In this paper, an effort has been made to study and
analyze different image enhancement techniques and thereby finding out the
better one for color image segmentation. Also, this comparative study is done
on two well-known color spaces HSV and LAB separately to find out which color
space supports segmentation task more efficiently with respect to those
enhancement techniques.
