Transductive video co-segmentation on the temporal trees

This paper proposes a novel multi-component video co-segmentation approach to simultaneously separate the foreground from the background in the video frames. To capture the variance of appearance of the foreground object, a multi-component foreground model is developed. Each component of the model characterizes a specific viewpoint/pose/appearance of the foreground object. To learn the parameters of the multi-component model, a transductive learning algorithm is leveraged to “transfer” the information of the labeled frames to the unlabeled frames in a tree-structured model, namely, temporal tree. Each branch of the temporal tree consists of the exemplars of a foreground component, and a transductive support vector regressor is capable of being trained. Experiments show that the proposed method outperforms quite a few state-of-the-art video segmentation algorithms in public benchmark.