  Graph-based video segmentation methods rely on superpixels as starting point.
While most previous work has focused on the construction of the graph edges and
weights as well as solving the graph partitioning problem, this paper focuses
on better superpixels for video segmentation. We demonstrate by a comparative
analysis that superpixels extracted from boundaries perform best, and show that
boundary estimation can be significantly improved via image and time domain
cues. With superpixels generated from our better boundaries we observe
consistent improvement for two video segmentation methods in two different
datasets.
