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Abstract
We present a novel mixture of trees probabilistic graphical model for semi-supervised video segmentation. Each component in this mixture represents a tree structured temporal linkage between super-pixels from the first to the last frame of a video sequence. We provide a variational inference scheme for this model to estimate super-pixel labels, their corresponding confidences, as well as the confidences in the temporal linkages. Our algorithm performs inference over full video volume which helps to avoid erroneous label propagation caused by using short time-window processing. In addition, our proposed inference scheme is very efficient both in terms of computational speed and use of RAM and so can be applied in real-time video segmentation scenarios. We bring out the pros and cons of our approach using extensive quantitative comparisons on challenging binary and multi-class video segmentation datasets.
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