Robust color image segmentation method based on weighting Fuzzy C-Means Clustering

A robust color image segmentation method based on weighting Fuzzy C-Means Clustering (RWFCM) is proposed for color image segmentation. The first component of color feature set 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. Feature distance which is applied to any structure of eigenvector space is applied instead of Euclidian distance to overcome the influence caused by structure of eigenvector space. Using a Color 1D Eigenvector-Gradient two-dimensional histogram automatically get the number of clusters, In order to remove the noise. Experiments show that the algorithm has better effect and lower computational complexity on color image segmentation.