Interactive RGB-D Image Segmentation Using Hierarchical Graph Cut and Geodesic Distance

In this paper, we propose a novel interactive image segmentation method for RGB-D images using hierarchical Graph Cut. Considering the characteristics of RGB channels and depth channel in RGB-D image, we utilize Euclidean distance on RGB space and geodesic distance on 3D space to measure how likely a pixel belongs to foreground or background in color and depth respectively, and integrate the color cue and depth cue into a unified Graph Cut framework to obtain the optimal segmentation result. Moreover, to overcome the low efficiency problem of Graph Cut in handling high resolution images, we accelerate the proposed method with hierarchical strategy. The experimental results show that our method outperforms the state-of-the-art methods with high efficiency.