Image segmentation is the division of a picture into various categories. One aspect of an image that adds information is color. As computer technology has advanced, the importance of image processing techniques has increased in a wider range of applications. In the field of image processing, picture segmentation is a well-known subject as well as a hotspot and the subject of several methodologies. Several all-purpose algorithms and methods have been developed for image segmentation. Cluster-based methods capable of utilizing the key attributes gathered became essential when color image segmentation started to develop. Consequently, CGFFCM means cluster-weight and group-local feature-weight learning in Fuzzy C-Means is the cluster-based pigment image segmentation method reported in this paper. Automatic cluster weighting is applied in CGFFCM to reduce sensitivity to clustering initiation, while group-local feature weighting is used to enhance picture segmentation. Additionally, it uses the efficient of image characteristics which consists of few features from the three separate categories to increase the quality of picture segmentation. The technical language of MATLAB is to be used for the CGFFCM implementation.