基于GrabCut改进的图像分割算法

针对GrabCut算法对于局部噪声敏感、耗时且提取边缘不理想等缺点，提出一种基于GrabCut改进的图像分割新算法。采用多尺度分水岭对梯度图像平滑去噪；对新梯度图像再次进行分水岭运算，不仅增强了图像的边缘点，还减少了后续处理的计算量；再用熵惩罚因子优化分割能量函数，抑制了目标信息的损失。实验结果表明，所提算法同传统算法的分割结果相比较，降低了错误率，增大了Kappa系数，提高了运行效率，并且，提取的边缘也更完整、平滑，适用于不同类型的图像分割。

Improved image segmentation algorithm based on GrabCut

To solve the problem that GrabCut algorithm is sensitive to local noise, time consuming and edge extraction is not ideal, the paper put forward a new algorithm of improving image segmentation based on GrabCut. Multi-scale watershed was used for gradient image smoothing and denoising. Watershed operation was proposed again for the new gradient image, which not only enhanced image edge points, but also reduced the computation cost of the subsequent processing. Then the entropy penalty factor was used to optimize the segmentation energy function to prevent target information loss. The experimental results show that the error rate of the proposed algorithm is reduced, Kappa coefficient is increased and the efficiency is improved compared with the traditional algorithm. In addition, the edge extraction is more complete and smooth. The improved algorithm is applicable to different types of image segmentation.