  Sampling-based alpha matting methods have traditionally followed the
compositing equation to estimate the alpha value at a pixel from a pair of
foreground (F) and background (B) samples. The (F,B) pair that produces the
least reconstruction error is selected, followed by alpha estimation. The
significance of that residual error has been left unexamined. In this letter,
we propose a video matting algorithm that uses L1-regularized reconstruction
error of F and B samples as a measure of the alpha matte. A multi-frame
non-local means framework using coherency sensitive hashing is utilized to
ensure temporal coherency in the video mattes. Qualitative and quantitative
evaluations on a dataset exclusively for video matting demonstrate the
effectiveness of the proposed matting algorithm.

Computer Vision and Pattern Recognition