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.