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Adaptive Occlusion Boundary Extraction for Depth Inference

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.