Unsupervised color image segmentation based on Gaussian mixture model
Yiming Wu, Xiangyu Yang, Kap Luk Chan · 2004
A novel color image segmentation method based on finite Gaussian mixture model is proposed in this paper. First, we use EM algorithm to estimate the distribution of input image data and the number of mixture components is automatically determined by MML criterion. Then the segmentation is carried out by clustering each pixel into appropriate component according to maximum likelihood (ML) criterion. The advantage of our method lies in its ability of less relying on initialization and segmenting images in a totally unsupervised manner. Experimental results show that our segmentation method can obtain better results than other methods.