Hmt-Contourlet Image Segmentation Based on Majority Vote

Abstract

Contourlet transform is a new multiscale and multidirectional image representation which effectively captures the edges and contours of images. Hidden Markov Tree model (HMT) can capture all inter-scale, interdirection and inter-location dependencies. Also, HMT can capture the statistical properties of the contourlet coefficients. Therefore, it is used to detect the image singularities (edges and ridges). In this paper, we have proposed three methods for texture segmentation, based on the HMT contourlet model. At first contourlet coefficient is computed and then, for each texture an HMT Contourlet model is trained for test phase, a set of decisions are made for each block of input image based on the maximum likelihood probability. Final decision will be based on the majority vote criterion. The proposed method has been examined on test images and promising results in terms of low segmentation errors has been obtained.