  Recently, significant progress has been achieved in deep image matting. Most
of the classical image matting methods are time-consuming and require an ideal
trimap which is difficult to attain in practice. A high efficient image matting
method based on a weakly annotated mask is in demand for mobile applications.
In this paper, we propose a novel method based on Deep Learning and Guided
Filter, called Inductive Guided Filter, which can tackle the real-time general
image matting task on mobile devices. We design a lightweight hourglass network
to parameterize the original Guided Filter method that takes an image and a
weakly annotated mask as input. Further, the use of Gabor loss is proposed for
training networks for complicated textures in image matting. Moreover, we
create an image matting dataset MAT-2793 with a variety of foreground objects.
Experimental results demonstrate that our proposed method massively reduces
running time with robust accuracy.
