Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
FOCS '99 Proceedings of the 40th Annual Symposium on Foundations of Computer Science
Convex Multi-class Image Labeling by Simplex-Constrained Total Variation
SSVM '09 Proceedings of the Second International Conference on Scale Space and Variational Methods in Computer Vision
Global Solutions of Variational Models with Convex Regularization
SIAM Journal on Imaging Sciences
Continuous Multiclass Labeling Approaches and Algorithms
SIAM Journal on Imaging Sciences
A Variational Framework for Region-Based Segmentation Incorporating Physical Noise Models
Journal of Mathematical Imaging and Vision
Optimality Bounds for a Variational Relaxation of the Image Partitioning Problem
Journal of Mathematical Imaging and Vision
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Variational relaxations can be used to compute approximate minimizers of optimal partitioning and multiclass labeling problems on continuous domains. While the resulting relaxed convex problem can be solved globally optimal, in order to obtain a discrete solution a rounding step is required, which may increase the objective and lead to suboptimal solutions. We analyze a probabilistic rounding method and prove that it allows to obtain discrete solutions with an a priori upper bound on the objective, ensuring the quality of the result from the viewpoint of optimization. We show that the approach can be interpreted as an approximate, multiclass variant of the coarea formula.