Extracting Primary Objects by Video Co-Segmentation
Video object segmentation is a challenging problem. Without human annotation or other prior information, it is hard to select a meaningful primary object from a single video, so extracting the primary object across videos is a more promising approach. However, existing algorithms consider the problem as foreground/background segmentation. Therefore, we propose an algorithm that learns the model of the primary object by representing the frames/videos as a graphical model. The probabilistic graphical model is built across a set of videos based on an object proposal algorithm. Our approach considers appearance, spatial, and temporal consistency of the primary objects. A new dataset is created to evaluate the proposed method and to compare it to the state-of-the-art on video object co-segmentation. The experiments show that our method obtains state-of-the-art results, outperforming other algorithms by 1.5% (pixel accuracy) on the MOViCS dataset and 9.6% (pixel accuracy) on the new dataset.