Abstract
Image matting is a fundamental computer vision problem and has many applications. Previous algorithms have
poor performance when an image has similar foreground
and background colors or complicated textures. The main
reasons are prior methods 1) only use low-level features and
2) lack high-level context. In this paper, we propose a novel
deep learning based algorithm that can tackle both these
problems. Our deep model has two parts. The first part is a
deep convolutional encoder-decoder network that takes an
image and the corresponding trimap as inputs and predict
the alpha matte of the image. The second part is a small
convolutional network that refines the alpha matte predictions of the first network to have more accurate alpha values
and sharper edges. In addition, we also create a large-scale
image matting dataset including 49300 training images and
1000 testing images. We evaluate our algorithm on the image matting benchmark, our testing set, and a wide variety
of real images. Experimental results clearly demonstrate
the superiority of our algorithm over previous methods