title: Image segmentation by MAP-ML estimations.
abstract: Image segmentation plays an important role in computer vision and image analysis. In this paper, image segmentation 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) and a graph cut algorithm is used to find the solution to the MAP estimation. The ML estimation is achieved by computing the means of region features in a Gaussian model. 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. Its results match image edges very well and are consistent with human perception. Comparing to six state-of-the-art algorithms, extensive experiments have shown that our algorithm performs the best.
authors.affiliation: Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China. sf.chen@sub.siat.ac.cn
authors.forename: Shifeng
authors.initials: S
authors.lastname: Chen
authors.forename: Liangliang
authors.initials: L
authors.lastname: Cao
authors.forename: Yueming
authors.initials: Y
authors.lastname: Wang
authors.forename: Jianzhuang
authors.initials: J
authors.lastname: Liu
authors.forename: Xiaoou
authors.initials: X
authors.lastname: Tang