Unsupervised segmentation based on multi-resolution analysis, robust statistics and majority game theory

Guodong Guo, Shan Yu, Songde Ma

Abstract: An unsupervised model-based image segmentation technique requires the model parameters for the various image classes in an observed image to be estimated directly from the image. The accuracy of the segmentation depends on the correct estimation of the parameters, as well as on the correct labeling of the pixels. In this work, the parameters are estimated by a multiresolution analysis on the histogram and a robust estimator using least median of squares. The labeling process is based on majority game theory. The method is tested in various synthetic and real images, showing its effectiveness.