A Global Sampling Based Image Matting Using Non-Negative Matrix Factorization

Abstract

Image matting is a technique in which a foreground is separated from the background of a given image along with the pixel wise opacity. This foreground can then be seamlessly composited in a different background to obtain a novel scene. This paper presents a global non-parametric sampling algorithm over image patches and utilizes a dimension reduction technique known as NMF (Non-Negative Matrix Factorization). Although some existing non-parametric approaches use large nearby foreground and background regions to sample patches but these approaches fail to take the whole image to sample patches. It is because of the high memory and computational requirements. The use of NMF in the proposed algorithm allows the dimension reduction which reduces the computational cost and memory requirement. The use of NMF also allow the proposed approach to use the whole foreground and background region in the image and reduces the patch complexity and help in efficient patch sampling. The use of patches not only allows the incorporation of the pixel colour but also the local image structure.
The use of local structures in the image is important to estimate a high-quality alpha matte especially in the images which have regions containing high texture. The proposed algorithm is evaluated on the standard data set and obtained results are comparable to the state-of-the-art matting techniques.