Robust Video Co-Segmentation Based on Co-saliency of Superpixels

In this paper, we propose a video co-segmentation based superpixel co-saliency. Our approach is based on a hypothesis, the common or similar objects in multiple video clips are salient, and they shares the similar feature. At first, we try to find out the regions in every clip which are salient and share the similar feature by proposing a new superpixel co-saliency scheme. Then, the most salient superpixels are chosen as the initial object marker superpixels. Starting from these superpixels, merge the neighboring and similar regions, and segment out the final object parts. The experimental results demonstrate that the proposed model can efficiently segment the common objects from a group of video clips with generally lower error rate than some existing methods.