Segmentation implies the division of an image into different objects or connected regions that do not overlap. Though, extensive research has been done in creating many different approaches and algorithms for image segmentation, however, it is still not very clear to assess whether one algorithm produces more accurate segmentations than another, whether it be for a particular image or set of images, or more generally, for a whole class of images [7]. A reliable and accurate segmentation of an image is, in general, very difficult to achieve by purely automatic means. Present researches on image segmentation using clustering algorithms reveals that K-means clustering algorithm so far produces best results but some improvements can be made to improve the results. The biggest disadvantage of our heavy usage of k-means clustering, is that it means we would have to think of a k each time, which really doesn’t make too much sense because we would like to algorithm to solve this on his own. Therefore we tried to find the K automatically and so create segmentation without any human giving “hints” to the algorithm. So we tried to make the process automatic. In this paper, the combined segmentation of RGB and HSV color spaces give more accurate segmentation result compared to segmentation of single color space. For keeping the k parameter as small as possible, we had to keep different intensity levels of the same color on the same segment to estimate the right k automatically for the algorithm.