User interactive segmentation with partially growing random forest
Jongwon Choi, Jin Young Choi · 2015
This paper proposes a novel approach for user interactive segmentation based on graph-cut, which improves the robustness against the initial parameter setting. The existing graph-cut based segmentation uses a parametric model to estimate the color distributions of foreground/background. However, the parametric model is sensitive to the predefined number of distribution models and can be easily biased by a wrong initialization. In this paper, we develop a non-parametric approach based on random forest to handle the biased initialization problem. In addition, we design a new structure of random forest referred to as partially growing random forest to reduce the training time. We compare the proposed approach quantitatively and qualitatively to the existing graph-cut based segmentation baseline, where our method shows a remarkable performance on the new colorful dataset as well as comparable results on the classical dataset.