  In this paper, we address the boundary detection task motivated by the
ambiguities in current definition of edge detection. To this end, we generate a
large database consisting of more than 10k images (which is 20x bigger than
existing edge detection databases) along with ground truth boundaries between
459 semantic classes including both foreground objects and different types of
background, and call it the PASCAL Boundaries dataset, which will be released
to the community. In addition, we propose a novel deep network-based
multi-scale semantic boundary detector and name it Multi-scale Deep Semantic
Boundary Detector (M-DSBD). We provide baselines using models that were trained
on edge detection and show that they transfer reasonably to the task of
boundary detection. Finally, we point to various important research problems
that this dataset can be used for.
