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...

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Bibliographic Details
Published in:IEEE transactions on pattern analysis and machine intelligence 2009-12, Vol.31 (12), p.2290-2297
Main Authors: Levinshtein, A., Stere, A., Kutulakos, K.N., Fleet, D.J., Dickinson, S.J., Siddiqi, K.
Format: Article
Language:English
Subjects:
Algorithms
Applied sciences
Approximation methods
Artificial intelligence
Boundaries
Computational efficiency
Computer science
control theory
systems
Constraining
Cost function
Density
Exact sciences and technology
Image databases
image labeling
Image segmentation
Labeling
Linear approximation
Pattern analysis
Pattern recognition. Digital image processing. Computational geometry
perceptual grouping
Pixel
Segments
Shape control
Superpixels
Training data
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Summary: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.
ISSN:0162-8828
1939-3539
DOI:10.1109/TPAMI.2009.96