基于图割的JPEG图像快速分割算法

Fast JPEG Image Segmentation Algorithm Based on Graph Cuts

基于图割理论的GrabCut算法由于使用所有像素来迭代估计高斯混合模型(GMM)参数,算法效率较低。针对该问题,提出一种基于图割的JPEG图像快速分割算法。以GrabCut算法为基础,对JPEG图像中DC系数构成的低频图像进行迭代分割,估计GMM参数以减少训练样本的数目。实验结果表明,该算法能在保证分割精度的前提下缩短高分辨率JPEG图像的分割时间。

GrabCut algorithm based on graph cuts is less efficient because it uses the whole pixels to estimate Gaussian Mixture Model（GMM） parameters by iteration.Aiming at this problem,this paper proposes a fast JPEG image segmentation algorithm based on graph cuts.On the basis of GrabCut algorithm,the Direct Current（DC） coefficients of JPEG image that constitute the low-frequency image are used to iteratively estimate the GMM parameters,which greatly reduce the number of training samples.Experimental results show that this algorithm can shorten the segmentation time of high-resolution JPEG image while preserving the segmentation accuracy.