Robust interactive image segmentation with automatic boundary refinement

We propose an effective image segmentation approach with a novel automatic boundary refinement procedure that requires little user interaction and makes the object cutout process more robust and convenient. It achieves these goals by the following three steps. First, merge over-segmented regions according to the maximal similarity rule using a few marking strokes as input. Second, detect possible erroneous low-contrast object boundaries by analyzing image content. Third, automatically refine those boundary regions using both local and global information. Experimental results are good even on very complex images.