  Natural image matting aims to precisely separate foreground objects from
background using alpha matte. Fully automatic natural image matting without
external annotation is quite challenging. Well-performed matting methods
usually require accurate handcrafted trimap as extra input, which is
labor-intensive and time-consuming, while the performance of automatic trimap
generation method of dilating foreground segmentation fluctuates with
segmentation quality. In this paper, we argue that how to handle trade-off of
additional information input is a major issue in automatic matting, which we
decompose into two subtasks: trimap and alpha estimation. By leveraging
easily-accessible coarse annotations and modeling alpha matte handmade process
of capturing rough foreground/background/transition boundary and carving
delicate details in transition region, we propose an intuitively-designed
trimap-free two-stage matting approach without additional annotations, e.g.
trimap and background image. Specifically, given an image and its coarse
foreground segmentation, Trimap Generation Network estimates probabilities of
foreground, unknown, and background regions to guide alpha feature flow of our
proposed Non-Local Matting network, which is equipped with trimap-guided global
aggregation attention block. Experimental results show that our matting
algorithm has competitive performance with current state-of-the-art methods in
both trimap-free and trimap-needed aspects.
