Hierarchical Supervoxel Graph for Interactive Video Object Representation and Segmentation

In this paper, we study the problem of how to represent and segment objects in a video. To handle the motion and variations of the internal regions of objects, we present an interactive hierarchical super-voxel representation for video object segmentation. First, a hierarchical supervoxel graph with various granularities is built based on local clustering and region merging to represent the video, in which both color histogram and motion information are leveraged in the feature space, and visual saliency is also taken into account as merging guidance to build the graph. Then, a supervoxel selection algorithm is introduced to choose supervoxels with diverse granularities to represent the object(s) labeled by the user. Finally, based on above representations, an interactive video object segmentation framework is proposed to handle complex and diverse scenes with large motion and occlusions. The experimental results show the effectiveness of the proposed algorithms in supervoxel graph construction and video object segmentation.