Learning Training Samples for Occlusion Edge Detection and Its Application in Depth Ordering Inference

This paper studies the problem of occlusion edge detection, which is applied to infer the depth order of objects in a monocular image. The key observation is that, given the fixed regression objective, the accuracy of occlusion edge detection is effectively boosted by selecting appropriate training samples in a discriminative feature subspace. Specifically, the ℓ
₁
-regularized logistic regression is employed to learn a more sparse yet discriminative feature subspace, while the training sample selection is formulated as a quadratic optimization with the robust Huber loss. The presented formulation avoids the noises efficiently, and hence the desirable occlusion edges can be detected. We validate the effectiveness of our approach on depth order inference problem. Experiments are conducted on two famous datasets, i.e., the Cornell depth-order dataset and the NYU2 dataset. Promising results demonstrate the superiority of our approach over the state-of-the-art approaches.