Application of Color Based Image Segmentation Paradigm on Rgb Color Pixels Using Fuzzy C-means and K Means Algorithms

Color is one of the properties which add information to the images. Classes of pixels are difficult to be identified when the color distributions of the different objects highly overlap in the color space and when the color points give rise to non-convex clusters. Color based image segmentation using fuzzy c means and k means algorithms can be used for the clustering of color image. This method is used to cluster and measure accuracy of the color images by segmenting each color pixels in the color images. Once segmentation is done, the fuzzy c means method is used for creating membership operation functions to define the degree to which a pixel belongs to an edge or a uniform region. The kmeans clustering is used to partition n data points into k clusters. This unsupervised clustering approaches has a strong affinity to get trapped into local minima while generating an optimal solution. Hence, it makes clustering wholly dependent on the distribution of primary cluster centre. This research work is employed to find the distance between color pixels of the RGB color spaces. Implementation has been done using MATLAB Simulation tool which generates the better result of this clustering algorithms.