关键词: 简单线性迭代聚类（SLIC）, 图割, 高斯混合模型

Abstract: GrabCut requires less user interaction and segmentation with high accuracy, but it is an iterative solver mode using GraphCuts which costs enormous time when processing high resolution images. This paper presents a fast GrabCut algorithm. To achieve acceleration, a simplified GraphCuts?model is constructed by SLIC algorithm in parameter estimation of Gauss mixture model. The original image?is?pre-segmented into super-pixel image with determining boundaries and internal high similarity through SLIC algorithm, a simplified network graph is constructed by this. The Gaussian Mixture Model （GMM） parameters can be estimated with the mean RGB values within blocks instead of all pixel values. To improve segmentation accuracy greatly, the GMM parameters obtained by the last step in parameter estimation of?Gauss mixture model are used to segment the original image. The experimental results demonstrate this algorithm with segmentation accuracy and computation efficiency.

Key words: Simple Linear Iterative Clustering（SLIC）, graph cuts, Gaussian mixture model