An Improved FCM algorithm for Color Image Segmentation

In this paper, an improved fuzzy C-means clustering (IFCM) algorithm for color image segmentation is proposed to solve the problem of heavy calculating burden and the disadvantage that clustering performance is affected by initial cluster centers for FCM, which is simple and easy to implement in color Image segmentation. For one thing, the quick subtractive clustering (QSC) is used for getting initial cluster centers of the image data points. For another, the first component of color feature set discovered by Ohta is chosen as the one-dimensional eigenvector. In order to reduce the computational complexity, the mapping from pixel space to eigenvector space is used for modifying the object function. Furthermore, combined the two problems of cluster centers initialization and cluster validity goes research to find optimizing the number of clusters. Experiments show that the proposed algorithm has better effect and lower computational complexity on color image segmentation.