Abstract
We present a novel, purely affinity-based natural image
matting algorithm. Our method relies on carefully defined
pixel-to-pixel connections that enable effective use of information available in the image and the trimap. We control
the information flow from the known-opacity regions into
the unknown region, as well as within the unknown region
itself, by utilizing multiple definitions of pixel affinities. This
way we achieve significant improvements on matte quality
near challenging regions of the foreground object. Among
other forms of information flow, we introduce color-mixture
flow, which builds upon local linear embedding and effectively encapsulates the relation between different pixel
opacities. Our resulting novel linear system formulation
can be solved in closed-form and is robust against several
fundamental challenges in natural matting such as holes
and remote intricate structures. While our method is primarily designed as a standalone natural matting tool, we
show that it can also be used for regularizing mattes obtained by various sampling-based methods. Our evaluation
using the public alpha matting benchmark suggests a significant performance improvement over the state-of-the-art