Name
Abstract | ||
|---|---|---|
In few years, min-cut/max-flow approach has become a leading method for solving a wide range of problems in computer vision. However, min-cut/max-flow approaches involve the construction of huge graphs which sometimes do not fit in memory. Currently, most of the max-flow algorithms are impracticable to solve such large scale problems. In this paper, we introduce a new strategy for reducing exactly graphs in the image segmentation context. During the creation of the graph, we test if the node is really useful to the max-flow computation. Numerical experiments validate the relevance of this technique to segment large scale images. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1016/j.endm.2011.05.012 | Electronic Notes in Discrete Mathematics |
Keywords | Field | DocType |
image segmentation,min-cut/max-flow,graph reduction | Graph,Combinatorics,Graph cuts in computer vision,Scale-space segmentation,Max-flow min-cut theorem,Computer science,Theoretical computer science,Image segmentation,Graph reduction,Computation | Journal |
Volume | ISSN | Citations |
37 | 1571-0653 | 2 |
PageRank | References | Authors |
0.36 | 5 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| N. Lermé | 1 | 6 | 1.15 |
| Lucas Létocart | 2 | 115 | 12.37 |
| François Malgouyres | 3 | 20 | 4.41 |