Learning based digital matting

Abstract

We cast some new insights into solving the digital matting problem by treating it as a semi-supervised learning task in machine learning. A local learning based approach and a global learning based approach are then produced, to fit better the scribble based matting and the trimap based matting, respectively. Our approaches are easy to implement because only some simple matrix operations are needed. They are also extremely accurate because they can efficiently handle the nonlinear local color distributions by incorporating the kernel trick, that are beyond the ability of many previous works. Our approaches can outperform many recent matting methods, as shown by the theoretical analysis and comprehensive experiments. The new insights may also inspire several more works.