Clustering segmentation algorithm based on expectation maximization for ink-jet printing image

ZHOU Jia-nan1， FENG Zhi-lin2， ZHU Xiang-jun2
（1. College of Information Engineering， Zhejiang Vocational College of Commerce， Hangzhou 310053， China；
2. College of Zhijiang， Zhejiang University of Technology， Hangzhou 310024， China）

Abstract： Aiming at the limitation for ink-jet printing images of clustering segmentation algorithm，a new image segmentation algorithm based on clustering technology combined with expectation-maximization was proposed. Firstly，the proposed segmentation algorithm was featured by incorporating spatial correlations，and the autoregressive model was applied to generate primitive homogeneous texture regions. Secondly，in order to improve the precision of parameter estimation for the segmentation model，a block-labeling mechanism was further introduced into the expectation-maximization algorithm，which can solve the maximum likelihood parameter estimation and deal with the parameter estimation of incomplete data. Finally，the image data sets were divided into some pieces of data block and went through clustering to make similar elements have a high similarity. After block clustering，pixels in the texture image were classified division for the following task of incorporating received result to achieve the correct segmentation of the target texture image. Experimental results show that， the proposed algorithm can provide a significant improvement over other common clustering segmentation methods to solve the ink-jet printing image segmentation problem.
Key words： ink-jet printing texture； expectation maximization（EM）； clustering segmentation algorithm（CSA）