Adaptive propagation matting based on transparency of image

Image matting is an essential technique in many image and video editing applications. Although many matting methods have been proposed, it is still a challenge for most to obtain satisfactory matting results in the transparent foreground region of an image. To solve this problem, this paper proposes a novel matting algorithm, i.e. adaptive transparency-based propagation matting (ATPM) algorithm. ATPM algorithm considers image matting from a new slant. We pay attention to the transparencies of the input images and creatively assign them into three categories (highly transparent, strongly transparent and little transparent) according to the transparencies of the foreground objects in the images. Our matting model can make relevant adjustment in terms of the transparency types of the input images. Moreover, many current matting methods do not perform well when the foreground and background regions have similar color distributions. Our method adds texture as an additional feature to effectively discriminate the foreground and background regions. Experimental results on the benchmark dataset show that our method gets high-quality matting results for images of three transparency types, especially provides more accurate results for highly transparent images comparing with the state-of-the-art methods.