Image Segmentation Using Inverted Dirichlet Mixture Model and Spatial Information

In this work, we present an unsupervised algorithm for Image segmentation using Inverted Dirichlet Mixture Model. The proposed approach uses image segmentation algorithm based on spatial information with the Inverted Dirichlet mixture model is presented. This method uses Markov Random Field to incorporate spatial information between neighboring pixels into a Inverted Dirichlet mixture model. The segmentation model is learned using Expectation Maximization (EM) algorithm based on Newton Raphson step. The obtained results using real image data set are more encouraging than those obtained using similar approaches.