Texture segmentation is one of the popular researchdomains and researchers across the globe are working on texturesegmentation to enhance segmentation performance to address itsrequirements in many fields. Color texture segmentation has widespectrum of applications in diverse fields such as segmentation ofnatural images, medical image analysis, remote sensing, shapeextraction and inspection of products etc. This paper presentscolor texture segmentation algorithm which can satisfyrequirements for such applications. Proposed algorithm is basedon Markov Random Field (MRF) model eliminating the need ofmajor contributor viz. Gabor filter used in past four decades forfeature extraction and use only color as texture feature. Highlycrude segmentation results are produced using only color astexture features. Crude segmentation results are enhanced byusing Median filter with enlarged window size quantitativelydetermined by using parameters viz. structural similarity index(SSIM), mean square error (MSE) and peak signal to noise ratio(PSNR). Feature space dimensions are reduced by factor of 11 inproposed approach and this reduced computations by a factor of11. The experimentation is carried out on 80 multi-class colortexture benchmark images from Prague texture segmentationdataset and 4 benchmark images in Vistex dataset. Meansegmentation accuracy achieved for Prague texture dataset is87.55% and it is higher by 9.82% over the best performingalgorithm among 11 state-of-art algorithms suggested in mostrecent literature. Accuracy achieved for Vistex dataset is 98.21%.Average SSIM for Prague dataset is 0.91403 and Vistex dataset is0.9405.