Segmentation of Color Image Using EM algorithm in HSV Color Space
Zhi-Kai Huang, Dehui Liu · 2007
This paper presents a new unsupervised method based on the Expectation-Maximization (EM) algorithm that we apply for color image segmentation. The method firstly Convert Image from RGB Color Space to HSV Color Space; Secondly we make use of a model of mixture K Gaussians, the Expectation Maximization (EM) formula is used to estimate the parameters of the Gaussian Mixture Model (GMM), which the desired number of partitions and fits the image histogram using a mixture of Gaussian distributions and provides a classified image; Thirdly, those pixels that have similar features will be regarded a group; Finally, for each group we segment pixels again according to their positions and we can get segmentation regions of the image. Experiment shows this method has better segmentation performance. The results of our methods are separately segmented and their combination allows the color image to be eventually partitioned.