A Robust Adaptive Fuzzy Clustering Segmentation Algorithm Based on Set Partition

Fuzzy C-means algorithm (FCM) is one of the most commonly used algorithms in image segmentation. In FCM, the initial cluster center and the number of clustering are determined in advance. To address this problem, a new adaptive fuzzy clustering algorithm (AFCM) is proposed. In AFCM algorithm, observation matrix, judgment matrix and set partitioning are used to select the appropriate clustering number automatically. In order to get better image segmentation effect, a robust adaptive fuzzy clustering algorithm (RAFCM) which using kernel distance as a similarity measure is proposed. The experimental results show that, compared with the FCM algorithm, the AFCM and RAFCM algorithms can not only determine the number of clustering automatically but also get better image segmentation quality.