Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Linear Programming Formulation and Approximation Algorithms for the Metric Labeling Problem
SIAM Journal on Discrete Mathematics
Variational Analysis in Sobolev and BV Spaces: Applications to PDEs and Optimization (Mps-Siam Series on Optimization 6)
Fast and exact primal-dual iterations for variational problems in computer vision
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
A First-Order Primal-Dual Algorithm for Convex Problems with Applications to Imaging
Journal of Mathematical Imaging and Vision
Exact optimization for Markov random fields with convex priors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Continuous Multiclass Labeling Approaches and Algorithms
SIAM Journal on Imaging Sciences
Diagonal preconditioning for first order primal-dual algorithms in convex optimization
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
| Hi-index | 0.00 |
We propose a novel convex prior for multilabel optimization which allows to impose arbitrary distances between labels. Only symmetry, d(i,j)≥0 and d(i,i)=0 are required. In contrast to previous grid based approaches for the nonmetric case, the proposed prior is formulated in the continuous setting avoiding grid artifacts. In particular, the model is easy to implement, provides a convex relaxation for the Mumford-Shah functional and yields comparable or superior results on the MSRC segmentation database comparing to metric or grid based approaches.