  Conventional video segmentation approaches rely heavily on appearance models.
Such methods often use appearance descriptors that have limited discriminative
power under complex scenarios. To improve the segmentation performance, this
paper presents a pyramid histogram based confidence map that incorporates
structure information into appearance statistics. It also combines geodesic
distance based dynamic models. Then, it employs an efficient measure of
uncertainty propagation using local classifiers to determine the image regions
where the object labels might be ambiguous. The final foreground cutout is
obtained by refining on the uncertain regions. Additionally, to reduce manual
labeling, our method determines the frames to be labeled by the human operator
in a principled manner, which further boosts the segmentation performance and
minimizes the labeling effort. Our extensive experimental analyses on two big
benchmarks demonstrate that our solution achieves superior performance,
favorable computational efficiency, and reduced manual labeling in comparison
to the state-of-the-art.
