Designing Effective Inter-Pixel Information Flow for Natural Image Matting


We present a novel, purely affinity-based natural image matting algorithm.

July 22, 2017
IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017



Yagiz Aksoy (Disney Research/ETH Joint PhD)

Tunc Aydin (Disney Research)

Marc Pollefeys (ETH Zurich)

Designing Effective Inter-Pixel Information Flow for Natural Image Matting


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. Amongst 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.

Copyright Notice