Interactive Image Matting using Optimization

In this paper, we formulate interactive image matting as a constrained optimization problem. We first make some simple and reasonable assumptions about the alpha matte and assume that, geometrically, the closer two pixels are, the more likely they will have similar alpha values, conversely, the farther apart two pixels are, the more likely they will have different alpha values; photometrically, the more similar two pixels are, the more likely they will have similar alpha values, conversely, the more different two pixels are, the more likely they will have different alpha values. We then formulate these assumptions in a quadratic matting cost function and obtain the alpha matte by minimizing the matting cost function. User interaction in the form of a few scribbles indicating a few definite background and foreground pixels is used to provide constraints to make the problem well posed. For a given set of constraints the matting cost function has a unique global minimum and can be solved efficiently using standard methods. With the computed alpha matte we then estimate the background and foreground pixels. Results show that the new method works effectively and provides an alternative computational algorithm for building interactive image editing tools.