  Superpixels serve as a powerful preprocessing tool in numerous computer
vision tasks. By using superpixel representation, the number of image
primitives can be largely reduced by orders of magnitudes. With the rise of
deep learning in recent years, a few works have attempted to feed deeply
learned features / graphs into existing classical superpixel techniques.
However, none of them are able to produce superpixels in near real-time, which
is crucial to the applicability of superpixels in practice. In this work, we
propose a two-stage graph-based framework for superpixel segmentation. In the
first stage, we introduce an efficient Deep Affinity Learning (DAL) network
that learns pairwise pixel affinities by aggregating multi-scale information.
In the second stage, we propose a highly efficient superpixel method called
Hierarchical Entropy Rate Segmentation (HERS). Using the learned affinities
from the first stage, HERS builds a hierarchical tree structure that can
produce any number of highly adaptive superpixels instantaneously. We
demonstrate, through visual and numerical experiments, the effectiveness and
efficiency of our method compared to various state-of-the-art superpixel
methods.
