关键词: 图像分割, 图谱聚类, 核方法, 交互图割, 最大期望（EM）算法

Abstract: There are two limitations with the graph cut models used in image segmentation. The parameters are predefined manually and a cut may end up with a local minimum. To overcome these limitations, an interactive kernel graph cut based upon EM algorithm is proposed. Image data is mapped into a kernel space and a new objective function is defined. Various parameters which are needed in the previous step are calculated according to the subimages; these two steps iterate until object function converges. Real and synthesis image segmentation experiments confirm it that the proposed algorithm is more precise and accurate compared with interactive graph cut based on normalized cut, especially when segmenting optical images; this algorithm can also keep detailed image information that cannot be captured by the normalized cut. Interactive kernel graph cut can offer a global optimum segmentation, and EM algorithm calculates various parameters needed in the graph cut. That’s why the word “adaptive” is used.

Key words: image segmentation, spectral cluster, kernel methods, interactive graph cut, Expectation-Maximization（EM） algorithm