  Image matting is generally modeled as a space transform from the color space
to the alpha space. By estimating the alpha factor of the model, the foreground
of an image can be extracted. However, there is some dimensional information
redundancy in the alpha space. It usually leads to the misjudgments of some
pixels near the boundary between the foreground and the background. In this
paper, a manifold matting framework named Patch Alignment Manifold Matting is
proposed for image matting. In particular, we first propose a part modeling of
color space in the local image patch. We then perform whole alignment
optimization for approximating the alpha results using subspace reconstructing
error. Furthermore, we utilize Nesterov's algorithm to solve the optimization
problem. Finally, we apply some manifold learning methods in the framework, and
obtain several image matting methods, such as named ISOMAP matting and its
derived Cascade ISOMAP matting. The experimental results reveal that the
manifold matting framework and its two examples are effective when compared
with several representative matting methods.
