  We propose a foreground segmentation algorithm that does foreground
extraction under different scales and refines the result by matting. First, the
input image is filtered and resampled to 5 different resolutions. Then each of
them is segmented by adaptive figure-ground classification and the best
segmentation is automatically selected by an evaluation score that maximizes
the difference between foreground and background. This segmentation is
upsampled to the original size, and a corresponding trimap is built.
Closed-form matting is employed to label the boundary region, and the result is
refined by a final figure-ground classification. Experiments show the success
of our method in treating challenging images with cluttered background and
adapting to loose initial bounding-box.
