Color Image Segmentation Based on Improved Affinity Propagation Clustering

The affinity propagation clustering algorithm requires huge storage and has high computational complexity.It is hard to be applied in image data real-time processing.A new color image segmentation method combining mean shift(MS) and affinity propagation(AP) named MSAP is presented in this paper.The proposed method preprocesses an input image with the MS algorithm.The numbers of segmented regions,instead of the numbers of image pixels,are considered as the input data scale of the AP algorithm.The average of the color vectors in each region is calculated as an input data point of AP algorithm.Distances between data points are regards as similarity measure indices,and then the AP algorithm is applied to perform globally optimized clustering and segmentation based on the similarity matrix.Experimental results illustrate that the MSAP method has superior performance and lower computational cost comparing with the AP algorithm.