title: TurboPixels: fast superpixels using geometric flows.
abstract: 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.
authors.affiliation: Department of Computer Science, University of Toronto, Toronto, ON, Canada. babalex@cs.toronto.edu
authors.forename: Alex
authors.initials: A
authors.lastname: Levinshtein
authors.forename: Adrian
authors.initials: A
authors.lastname: Stere
authors.forename: Kiriakos N
authors.initials: KN
authors.lastname: Kutulakos
authors.forename: David J
authors.initials: DJ
authors.lastname: Fleet
authors.forename: Sven J
authors.initials: SJ
authors.lastname: Dickinson
authors.forename: Kaleem
authors.initials: K
authors.lastname: Siddiqi