Natural image matting via adaptive local and nonlocal sample clustering

Digital image matting is the determination of foreground color, background color, and an opacity value of each pixel for an input image. Inherently, matting is a highly ill-posed and under-constrained problem. Thus, some assumptions need to be made to resolve it. Inspired by closed-form matting and color clustering matting, in this work, we first develop an adaptive sample clustering criterion to automatically assign either local or nonlocal neighborhood to each pixel. After that, in order to enhance matting accuracy, we improve the nonlocal clustering performance by introducing a new feature selection parameter to choose preferred feature space for different images in a fully automatic way. And finally we solve the problem using a closed form solution. Experimental results show that our algorithm achieves equal or even better performance among many state-of-the-art matting techniques.