Alpha matting with image pixel correlation

Alpha matting aims at estimating the foreground opacity matte in an image. It is critical to find the best known samples for foreground and background color of unknown pixels in color sampling-based matting approaches. Most matting approaches can only select color sample for each pixel each time, which prevent them from maintaining the same correlation in image pixels. In particular, they may fail to collect appropriate samples from complex images and thus lead to artifacts. In order to solve the problem, we present a correlation-based sampling method in which the image pixel correlation is employed in color sampling and optimal sample selection for the alpha matte estimation of the image. First, the foreground and background colors of sample set can completely cover the color of unknown pixels to avoid missing the true samples. This is accomplished by artificial immune network adaptively learning the image correlation in unknown pixels. Besides, we propose the sample selection process as a global optimization problem with image correlation. All unknown pixels are treated as a high-dimensional input variable, particle swarm optimization algorithms is employed to solve the global optimization problem selecting the best sample pairs for all unknown pixels. The experimental study on images dataset shows that image pixels correlation is effective to improve matting, and that our matting results are comparable to some recent approaches.