Interactive Image Segmentation on Multiscale Appearances
Kun He, Dan Wang, Miao Tong, Xu Zhang · IEEE Access · 2018
Interactive segmentation algorithms based on graph cuts can extract the foreground successfully from a simple scene. However, they are ineffective for complex-scene images. To improve the segmentation performance, we propose an interactive segmentation algorithm, which combines the segmentation and the multiscale smoothing into a unified model. This model consists of the segmentation and the smoothing. The segmentation relies on the multiscale appearances, which depend on the smoothing. In the smoothing part, the total variation is used to preserve the geometric shape of the foreground and captures different scale edges and appearances for segmentation. Combining the multiscale edges and appearances, we propose a novel Gibbs energy functional for segmentation. The exact global minima of the energy can be found by jointing the image smoothing and the optimization of segmentation. In this algorithm, the smoothing motivates that the foreground could be detected easily from a proper scale. Experimental results on the BSD300 data set and Weizmann horse’s database indicate that, compared with the existing interactive segmentation algorithms, the proposed algorithm provides competitive performance in terms of segmentation accuracy.