Image Segmentation is an imperative part of computer perception and it stills a difficult and thriving subject for machine and computer perception. Research in computer vision proves that the segmenting of an image into various understandable regions for subsequent post processing (e.g., scene understanding, pattern recognition) is an ambitious task.In this paper, the proposed system uses various segmentation methods to increase the accuracy of segmentation and satisfactory visual entirety. The segmentation method is based on watershed, modified RAG-RKM and SLIC method. Using the Watershed segmentation we find how many pixels are allotted to which regions. Using modified RAGRKM method we find the mask and seed pixels using which the contour is plotted with the help of pixel value and angle of direction. The SLIC algorithm is used to find the segmentation of image using the angle of movement which takes into consideration the neighbourhood distance, edges of the regions and the area of the region. The proposed algorithm is applied on BSD300 database which contains 300 natural images. The accuracy of the proposed system is very high. Also the algorithm was applied on random color-texture images and the result found was good.