Name
Abstract | ||
|---|---|---|
Unsupervised over-segmentation of an image into superpixels is a common preprocessing step for image parsing algorithms. Superpixels are used as both regions of support for feature vectors and as a starting point for the final segmentation. Recent algorithms that construct superpixels that conform to a regular grid (or superpixel lattice) have used greedy solutions. In this paper we show that we can construct a globally optimal solution in either the horizontal or vertical direction using a single graph cut. The solution takes into account both edges in the image, and the coherence of the resulting superpixel regions. We show that our method outperforms existing algorithms for computing superpixel lattices. Additionally, we show that performance can be comparable or better than other contemporary segmentation algorithms which are not constrained to produce a lattice. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/CVPR.2010.5539890 | 2010 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR) |
Keywords | Field | DocType |
cost function,lattices,topology,greedy algorithms,feature vector,image segmentation,layout,computer vision,strips,graph theory,segmentation,graph cut,global optimization,pipelines | Cut,Graph theory,Feature vector,Scale-space segmentation,Pattern recognition,Segmentation,Computer science,Segmentation-based object categorization,Image segmentation,Greedy algorithm,Artificial intelligence | Conference |
Volume | Issue | ISSN |
2010 | 1 | 1063-6919 |
Citations | PageRank | References |
26 | 1.09 | 22 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alastair P. Moore | 1 | 59 | 4.07 |
| Simon Prince | 2 | 914 | 60.61 |
| Jonathan Warrell | 3 | 494 | 18.95 |