Applications of parametric maxflow in computer vision

The maximum flow algorithm for minimizing energy functions of binary variables has become a standard tool in computer vision. In many cases, unary costs of the energy depend linearly on parameter . In this paper we study vision applications for which it is important to solve the maxflow problem for different 's. An example is a weighting between data and regularization terms in image segmentation or stereo: it is desirable to vary it both during training (to learn from ground truth data) and testing (to select best using high-knowledge constraints, e.g. user input). We review algorithmic aspects of this parametric maximum flow problem previously unknown in vision, such as the ability to compute all breakpoints of and corresponding optimal configurations in finite time. These results allow, in particular, to minimize the ratio of some geometric functionals, such as flux of a vector field over length (or area). Previously, such functionals were tackled with shortest path techniq...

• Type: Conference
• Year: 2007
• Where: ICCV
• Authors: Vladimir Kolmogorov, Yuri Boykov, Carsten Rother
• Keywords: Computer Vision, Geometric Functionals, ICCV 2007, Image Segmentation, Maximum Flow Algorithm, Maximum Flow Problem, Shortest Path Techniques