Oriented Edge Forests for Boundary Detection

We present a simple, efficient model for learning bound- ary detection based on a random forest classifier. Our ap- proach combines (1) efficient clustering of training exam- ples based on a simple partitioning of the space of local edge orientations and (2) scale-dependent calibration of in- dividual tree output probabilities prior to multiscale combi- nation. The resulting model outperforms published results on the challenging BSDS500 boundary detection bench- mark. Further, on large datasets our model requires sub- stantially less memory for training and speeds up training time by a factor of 10 over the structured forest model.