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Segmentation is an important image processing techn ique that helps to analyze an image automatically. Applications involving detection or recognition of objects in images often include segmentation process. This paper describes two unsupervised clustering based color image segmentation technique s namely K-means clustering and Fuzzy C-means (FCM) clustering. The advantages and disadvantages of both K-means and Fu zzy C-means algorithm are also presented in this pa per. K-means algorithm takes less computation time as compared t o Fuzzy C-means algorithm which produces result clo se to that of K-means. On the other hand in FCM algorithm each pixel of an image can have membership to more than one cluster which is not in case of K-means algorithm, an advantage to FCM method. C olor images contain wide variety of information and are more complicated than gray scale images. In image proces sing, though color image segmentation is a challeng ing task but provides a path for image analysis in practical application fi elds. Secondly some novel approaches to FCM algorit hm for better image segmentation are also discussed such as SFCM (Spati al FCM) and THFCM (Thresholding FCM). Basic FCM alg orithm does not take into consideration the spatial information of the image. SFCM specially focus on spatial details and contribute towards image segmentation results for image analysis. It i ntroduces spatial function into FCM algorithm membe rship function and then operates with available spatial information. THFCM is another approach that focus on thresholding tech nique for image segmentation. It main task is to find a discerner c luster that will act as automatic threshold. These two approaches shows how better segmentation results can be obtained.

SURVEY ON CLUSTERING BASED COLOR IMAGE SEGMENTATION AND NOVEL APPROACHES TO FCM ALGORITHM