Mean shift is a popular method to segment images and
videos. Pixels are represented by feature points, and the
segmentation is driven by the point density in feature space.
In this paper, we introduce the use of Morse theory to interpret
mean shift as a topological decomposition of the
feature space into density modes. This allows us to build
on the watershed technique and design a new algorithm to
compute mean-shift segmentations of images and videos. In
addition, we introduce the use of topological persistence to
create a segmentation hierarchy. We validated our method
by clustering images using color cues. In this context,
our technique runs faster than previous work, especially
on videos and large images. We evaluated accuracy with
a classical benchmark which shows results on par with existing
low-level techniques, i.e. we do not sacrifice accuracy
for speed.
