Semi-supervised Image Segmentation based on Integration of SSFCM with Random Walks

Algorithms based on semi-supervised clustering are suitable to segment images containing a large amount of objects with the similar color features,but they cannot gain ideal effects to images containing noises;semi-supervised image segmentation algorithm based on random walks theory requires the user to label all objects contained in the image.A semi-supervised image segmentation algorithm to solve this problem is presented,which is based on integration of semi-supervised fuzzy c-means clustering algorithms with random walks.It models the image's color feature through semi-supervised c-means clustering algorithm(SSFCM) based label data,then it defines a reliability function based on the membership calculated by SSFCM,and the pixels are classified into two types that are considered as labeled and unlabeled pixels of Random Walks.The experimental results indicate that the algorithm 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.