Iterative MAP and ML Estimations for Image Segmentation

Abstract

Image segmentation plays an important role in computer vision and image analysis. In this paper, the segmentation problem is formulated as a labeling problem under a probability maximization framework. To estimate the label configuration, an iterative optimization scheme is proposed to alternately carry out the maximum a posteriori (MAP) estimation and the maximum-likelihood (ML) estimation. The MAP estimation problem is modeled with Markov random fields (MRFs). A graph-cut algorithm is used to find the solution to the MAP-MRF estimation. The ML estimation is achieved by finding the means of region features. Our algorithm can automatically segment an image into regions with relevant textures or colors without the need to know the number of regions in advance. In addition, under the same framework, it can be extended to another algorithm that extracts objects of a particular class from a group of images. Extensive experiments have shown the effectiveness of our approach.