  We present Convolutional Oriented Boundaries (COB), which produces multiscale
oriented contours and region hierarchies starting from generic image
classification Convolutional Neural Networks (CNNs). COB is computationally
efficient, because it requires a single CNN forward pass for contour detection
and it uses a novel sparse boundary representation for hierarchical
segmentation; it gives a significant leap in performance over the
state-of-the-art, and it generalizes very well to unseen categories and
datasets. Particularly, we show that learning to estimate not only contour
strength but also orientation provides more accurate results. We perform
extensive experiments on BSDS, PASCAL Context, PASCAL Segmentation, and
MS-COCO, showing that COB provides state-of-the-art contours, region
hierarchies, and object proposals in all datasets.
