Foreground segmentation with single reference frame using iterative likelihood estimation and graph-cut
Keita Takahashi, Taketoshi Mori · 2008
This paper introduces a new foreground segmentation method. In contrast to most of the related works, our method uses only two image frames, a target frame to process, and a single reference frame. Our method first conducts simple thresholding like background subtraction, but then applies an iteration scheme we propose to estimate the pixel-wise likelihood of belonging to the foreground/background from the frame-to-frame difference. Finally, a further refinement considering edges is applied using graph-cut optimization. Experimental results show the effectiveness of our method, especially in that it keeps good performance over a wide range of the threshold value. That consistent performance will become an important step toward fully-automatic segmentation.