Progressive Cut: An Image Cutout Algorithm that Models User Intentions

In this article, we presented a progressive-cut algorithm for background and foreground segmentation. We first analyzed the user intention behind the additional user-specified stroke, and then incorporated the user intention into the graph-cut framework: we derived an eroded graph to prevent overshrinkage, and added a user attention term to the energy function to compress overexpansion in low-interest areas. Experiments showed that the new algorithm outperforms existing graph-cut methods in both accuracy and speed, and it effectively removes the fluctuation effect, making results more controllable with fewer strokes.