TurboPixels: fast superpixels using geometric flows.
We describe a geometric-flow-based algorithm for computing a dense oversegmentation of an image, often referred to as superpixels. It produces segments that, on one hand, respect local image boundaries, while, on the other hand, limiting undersegmentation through a compactness constraint. It is very fast, with complexity that is approximately linear in image size, and can be applied to megapixel sized images with high superpixel densities in a matter of minutes. We show qualitative demonstrations of high-quality results on several complex images. The Berkeley database is used to quantitatively compare its performance to a number of oversegmentation algorithms, showing that it yields less undersegmentation than algorithms that lack a compactness constraint while offering a significant speedup over N-cuts, which does enforce compactness.
Levinshtein
Alex
A
Department of Computer Science, University of Toronto, Toronto, ON, Canada. babalex@cs.toronto.edu
Stere
Adrian
A
Kutulakos
Kiriakos N
KN
Fleet
David J
DJ
Dickinson
Sven J
SJ
Siddiqi
Kaleem
K