Complementary saliency driven co-segmentation with region searching and Hierarchical constraint

Foreground histogram consistency is a commonly used global constraint for co-segmentation problems. However, scale, target posture as well as the viewpoint changes usually make it hard to guarantee the absolute consistency of histogram. In this paper, we formulate a unified framework by incorporating the local region searching strategy and hierarchical constraint with the global scale-invariant framework. Additionally, complementary saliency is adopted to reduce the unreliable prediction brought by single saliency estimation method. Such automatic foreground inferring strategy also shows good compatibility and interaction with the new unified co-segmentation framework. The comparison experiments conducted on the public datasets demonstrate the good performance of the proposed method.