Semi-supervised image segmentation combining SSFCM and Random Walks

We present a semi-supervised image segmentation algorithm to segment the noisy image that includes a large amount of objects with the same color features. It models the image's color feature through SSFCM based labeled data, and then it defines a reliability function based upon the membership calculated by SSFCM, and the pixels are classified as two types that are considered as labeled and unlabeled pixels of Random Walks, at last it performs Random Walks to produce the final segmentation. The experimental results show the effectiveness of our algorithm. It not only reduces the noise sensitivity of SSFCM but also avoids cumbersome operations that the user labels the seed points of all objects for Random Walks.