Supervised video object segmentation using a small number of interactions

A supervised video object segmentation algorithm using a small number of interactions is proposed. The proposed algorithm is composed of three steps: semi-automatic first frame segmentation; automatic object tracking; boundary refinement. Homogeneous region segmentation is performed before the user interaction for the first frame to minimize the amount of interaction. Then, a polygon with very few key nodes can be drawn conveniently to get the segmentation mask of the first frame. In the object region tracking, pixel-wise backward tracking is adopted. Finally, a method for mask refinement is proposed by considering similar pixels in the neighborhood of each pixel. Extensive experimental results show that the proposed algorithm is effective for video object segmentation semiautomatically.