Reduced graphs for min-cut/max-flow approaches in image segmentation

Nicolas Lermé (1, 2) , Lucas Létocart (2) , François Malgouyres (1)

1 LAGA - Laboratoire Analyse, Géométrie et Applications
2 LIPN - Laboratoire d'Informatique de Paris-Nord

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.

Domaines: Mathématique discrète [cs.DM]

Nicolas Lermé, Lucas Létocart, François Malgouyres. Reduced graphs for min-cut/max-flow approaches in image segmentation. LAGOS'11 : VI Latin-American Algorithms, Graphs, and Optimization Symposium, Mar 2011, Bariloche, Argentina. 6 p. ⟨hal-00596000⟩