The Normalized Cut (NCut) model is a popular graph-based model for image segmentation. But it suffers from the excessive normalization problem and weakens the small object and twig segmentation. In this paper, we propose an Explored Normalized Cut (ENCut) model that establishes a balance graph model by adopting a meaningful-loop and a k-step random walk, which reduces the energy of small salient region, so as to enhance the small object segmentation. To improve the twig segmentation, our ENCut model is further enhanced by a new Random Walk Refining Term (RWRT) that adds local attention to our model with the help of an un-supervising random walk. Finally, a move-making based strategy is developed to efficiently solve the ENCut model with RWRT. Experiments on three standard datasets indicate that our model can achieve state-of-the-art results among the NCut-based segmentation models.

In this paper, we propose an adaptive occlusion boundary extraction method for depth inference based on an adaptive segmentation and classification. First, an Adaptive DRW is proposed to generate more precise seeds and adaptive segmentation results, which can improve the feature quality and lower the boundary imbalance degree. Then, to deal with the imbalanced classification, we design a cost-sensitive boosting method-Adaptive AdaCost to better classify the imbalanced boundary, which can further improve overall performance and lower the cumulative misclassification cost and cost upper bound. Benefited from our Adaptive DRW and AdaCost, we extract more reliable and precise occlusion boundaries and use them for depth inference. The experiment results demonstrate that the combination of our Adaptive DRW and Adaptive AdaCost can produce more precise occlusion boundaries, and the depth inference result with our occlusion boundaries can be greatly improved.