Graph-to-graph energy minimization for video object segmentation

We describe a new unsupervised video object segmentation (VOS) method based on the graph-to-graph energy minimization, which focuses on exploiting the mutual bootstrapping information between bottom-up (i.e., using pixel/superpixel attributes) and top-down (i.e., using learned appearance and motion cues) processes in a uni-fiedframework. Specifically, we construct a graph-to-graph energy function to encode the spatial similarities among superpixels (superpixel-graph) and temporal consistency among regions (region-graph). An efficient heuristic iterative algorithm is used to minimize the energy function to get the optimal assignment of superpixel and region labels to complete the VOS task. Experiments on two challenging benchmarks (i.e., SegTrack v2 and DAVIS) show that the proposed method achieves favorable performance against the state-of-the-art unsupervised VOS methods and comparable performance with the state-of-the-art semi-supervised methods.