Unsupervised Image Segmentation Method based on Finite Generalized Gaussian Distribution with EM & K-Means Algorithm

In Image Processing Model Based Image Segmentation plays a dominant role in Image Analysis and Image Retrieval. Recently much work has been reported regarding Image Segmentation based on Finite Gaussian Mixture Models using EM algorithm. (Yiming Wu et al (2003)), (Yamazaki. T (1998)). However, in some images the pixel intensities inside the image regions may not be Meso-Kurtic or Bell Shaped, because of the Non-Gaussian nature. Hence there are some situations where Image Segmentation is to be done with a more Generalized Finite Mixture Distribution. In this article we develop and analyze an image segmentation method based on Finite Generalized Gaussian Mixture Model using EM and K-Means algorithm. The K-Means algorithm is utilized to obtain the number of regions and the initial estimates of the model parameters. The update equations of the model parameters are obtained by using the EM algorithm. The segmentation of the pixels in the image is done by maximizing the component likelihood function. The performance of this method is evaluated through real time data on 3 images by calculating misclassification rate and image quality metrics. It is observed that the proposed method performs much superior to the earlier image segmentation methods.