TurboPixels: Fast Superpixels Using Geometric Flows
University of Toronto · McGill University · +1 more institution
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…
Citation impact
- FWCI
- 32.83
- Percentile
- 100%
- References
- 31
Authors
6Topics & keywords
- Compact space
- Constraint (computer-aided design)
- Image (mathematics)
- Limiting
- Speedup
- Artificial intelligence
- Computer science
- Computer vision