Color image segmentation by partitional clustering algorithms

This paper presents the results of some partitional clustering algorithms applied to the segmentation of color images in the RGB space. As more information is involved in the algorithm, and the distance measure is more flexible, the better the results. The selected algorithms for this work are the K-means, the FCM, the GK-B, and the GKPFCM. The GKPFCM gives the better results when all the algorithms are applied to the segmentation of two images, an image of bananas and the other one of tomates at different stages of ripeness in both cases. The results are interesting as it is possible to identify the objects, to determine the degree of ripeness, and to estimate the amount and proportion of ripe objects for a possible decision-making.