基于四叉树分解与图割的彩色图像快速分割

图割是一种基于图论的组合优化方法,基于图割的GrabCut是一种高效的前景提取算法。然而,GrabCut为达到一定分割精度,在高斯混合模型参数估计过程中多次迭代使用图割,这使得GrabCut在处理海量级图像数据时,耗时往往比较大。通过四叉树分解,可以将图像划分成区域内相似度高的若干分块,以构建精简的网络图,并用块内的RGB均值代替该块内的所有像素点的值进行高斯混合模型参数估计,从而减小问题规模,提高算法效率。实验结果表明了算法的可行性及有效性。

Fast segmentation in color image based on quadtree decomposition and graph cuts

Graph cuts is a combinatorial optimization method based on graph theory,and GrabCut based on it is an efficient foreground extraction algorithm.To achieve a certain segmentation accuracy,multiple iterations using graph cuts during the course of parameter estimation in the Gaussian Mixture Model(GMM),make it consume massive time when processing a great deal of image data.In this paper,we use quadtree decomposition to divide the image into several sub blocks with internal high similarity and thus construct a compact weighted graph;the Gaussian Mixture Model (GMM) parameters can be estimated by the mean RGB values instead of all pixel values within blocks,so it can reduce the problem scale and significantly improve the efficiency of the algorithm.The experimental results demonstrate the feasibility and effectiveness of the algorithm.