Abstract: There are some problems with the color image segmentation technology based on traditional Fuzzy C-means clustering algorithm,such as the selection of the initial category number,the determinated of the initial centroids,large amount of calculation in clustering process and post-processing.Based on the research of these problems,according to the shortage of random initialization in traditional FCM,and for getting more accurate initialization automatically,this paper proposed a clustering segmentation method combining Hill-climbing for color image(HFCM),which can generate the initial centroids and the number of clusters adaptively according to the three dimensional histogram of the image.In addition,a new post-processing strategy which combined the most frequency filter and region mergeing was introduced to effectively eliminate small spatial regions.Experiments show that the proposed segmentation algorithm achieves high computational speed,and its segmentation results are close to human perceptions.

Key words: Color image segmentation, Fuzzy C-means clustering algorithm, Global three-dimensional color histogram, Hill-climbing algorithm