VIDEO OBJECT CO-SEGMENTATION FROM NOISY VIDEOS BY A MULTI-LEVEL HYPERGRAPH MODEL

Defined as simultaneously segmenting a set of related videos to identify the common objects, video co-segmentation has attracted the attention of researchers in recent years. Existing methods are primarily based on pair-wise relations between adjacent pixels/regions, which are susceptible to performance degradation from "empty" video frames (e.g., due to transient/intermittent common objects). In this paper, a new multi-level hypergraph based method, termed the full Video object Co-Segmentation method (VCS), is proposed, which incorporates both a high-level semantics object model and a low-level appearance/motion/saliency object model to construct the hyperedge among multiple spatially and temporally adjacent regions. Specifically, the high-level semantic model fuses multiple object proposals from each frame instead of relying on a single object proposal per frame. A hypergraph cut is subsequently utilized to calculate the object co-segmentation. Experiments on three datasets demonstrate the efficacy of the proposed VCS method.