  Superpixel segmentation is becoming ubiquitous in computer vision. In
practice, an object can either be represented by a number of segments in finer
levels of detail or included in a surrounding region at coarser levels of
detail, and thus a superpixel segmentation hierarchy is useful for applications
that require different levels of image segmentation detail depending on the
particular image objects segmented. Unfortunately, there is no method that can
generate all scales of superpixels accurately in real-time. As a result, a
simple yet effective algorithm named Super Hierarchy (SH) is proposed in this
paper. It is as accurate as the state-of-the-art but 1-2 orders of magnitude
faster. The proposed method can be directly integrated with recent efficient
edge detectors like the structured forest edges to significantly outperforms
the state-of-the-art in terms of segmentation accuracy. Quantitative and
qualitative evaluation on a number of computer vision applications was
conducted, demonstrating that the proposed method is the top performer.
