基于超像素和随机游走的交互式分割算法

Interactive segmentation algorithm based on superpixel and random walk

交互式图像分割通过先验信息指导获取图像中人们感兴趣的部分,但是现有算法无法在效率和精度上实现平衡。为了解决此问题,提出了一种基于超像素和随机游走的快速交互式分割算法(random walk on superpixel,SPRW)。首先,将图像预分割为具有局部相似性的超像素区域,使用像素颜色均值对超像素区域表示;其次,根据人工标记的先验信息建立F-B图结构,扩展随机游走的范围,并使用随机游走的方法求解,获得硬分割结果;最后,针对分割结果的边界不光滑问题,提出改进的抠图算法(fast robust matting,FRB)进行二次处理,得到软分割结果。在BSD500和MSRC数据集上的实验证实,所提出的硬分割方法与其他算法在时间和平均交并比等指标上有较大优势;在Alpha Matting数据集上的实验充分证实所提出的软算法在提高效率的同时精度也有一定的提升;此外,在生活照更换背景的实验上展现了该算法的应用价值。

Interactive image segmentation uses a priori information to guide the acquisition of the people’s interesting parts of an image,but existing algorithms cannot balance efficiency and accuracy.To solve this problem,this paper proposed a fast interactive segmentation algorithm based on superpixel and random walk(random walk on superpixel,SPRW).Firstly,it pre-segmented the image into superpixel regions with local similarity and used pixel colour averages to represent the superpixel regions.Secondly,it built the F-B graph structure based on manually labelled a priori information and extended the range of the random walk,obtained the hard segmentation results using the random walk solution.Finally,this paper proposed the improved matting algorithm(fast robust matting,FRB)for the unsmooth boundaries of segmentation results and performed the secondary process to obtain soft segmentation results.Experiments on the BSD500 and MSRC datasets confirm that the proposed hard method is superior to other algorithms in terms of time and average intersection ratio,while experiments on the Alpha Matting dataset demonstrate that the proposed soft algorithm improves efficiency and accuracy.In addition,the experiment of replacing the background of a life photograph demonstrates the application value of the proposed algorithm.