To reduce labors such as user input

this paper proposes a simple stroke-based iterative image matting approach

which only needs a few user scribbles to mark foreground and background pixels. It builds robust color models from multiple feature spaces of pixels

and further introduces sampling constraint functions to collect valid foreground and background samples. By combing samples information and local smoothness constraint

the method defines a global energy function on all unknown pixels. The optimal matte is accomplished with iterative energy minimization. The experimental results demonstrated that the method can produce accurate and visually smooth results and is efficient to handle texture-rich images or images with ambiguous foreground and background colors.