A global non-parametric sampling based image matting

Image matting is a process of separating the foreground objects from an image along with opacity values for each pixel. It is an under-constraint problem hence a user interaction is required to identify the definite foreground, background and semi-transparent pixels. In general the information in the definite foreground and background regions is modeled locally to estimate the foreground and background color of the pixels in the semi-transparent region which are then used to estimate opacity values. A global non-parametric sampling based approach is presented which incorporates not only the color information in the foreground and background regions but also utilizes the local structure of an image to improve the quality of the estimate matte. This global sampling approach reduces the segmentation mis-classification that is incorporated in the resulting alpha matte by considering only the local color information. The results obtained are comparable to the state of the art image matting techniques on a standard dataset.