Levinstein, A., Stere, A., Kutulakos, K., Fleet, D.J., Dickinson, S. and Sidiqqi, K.
TurboPixels: Fast superpixels using geometric flows.
IEEE Transactions on Pattern Analysis and Machine Intelligence,
(in press), 2009
We describe a geometric-flow based algorithm for
computing a dense over-segmentation of an image, often referred
to as superpixels. It produces segments that on one hand
respect local image boundaries, while on the other hand limit
under-segmentation 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 over-segmentation algorithms,
showing that it yields less under-segmentation than algorithms
that lack a compactness constraint, while offering a significant
speed-up over N-cuts, which does enforce compactness.
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