Three-layer graph framework with the sumD feature for alpha matting

Alpha matting, the process of extracting opacity mask of the foreground in an image, is an important task in image and video editing. All of the matting methods need exploit the relationships between pixels. The traditional propagation-based methods construct constrains based on nonlocal principle and color line model to reflect the relationships. However, these methods would produce artifacts if the constrains are not reliable. So we improve this problem in three points. Firstly, we design a novel feature called sumD feature to increase the pixel discrimination. This feature is simple and could encourage pixels with similar texture to have similar feature values. Secondly, we design a three-layer graph framework to construct nonlocal constrains. This framework finds constrains in multi-scale range and selects reliable constrains, then unifies nonlocal constrains according to their reliabilities. Thirdly, we develop a new label extension method to add hard constrains. Experimental results confirm that the effectiveness of the three changes, and the proposed method achieves high rank on the benchmark dataset.