79 citations · 123 across the 7 of their papers we have counts for
7 papers
Multicuts and Perturb & MAP for Probabilistic Graph Clustering
Jörg Hendrik Kappes, Paul Swoboda, Bogdan Savchynskyy +2
We present a probabilistic graphical model formulation for the graph clustering problem. This enables to locally represent uncertainty of image partitions by approximate marginal d…
Partial Optimality by Pruning for MAP-Inference with General Graphical Models
Paul Swoboda, Alexander Shekhovtsov, Jörg Hendrik Kappes +2
We consider the energy minimization problem for undirected graphical models, also known as MAP-inference problem for Markov random fields which is NP-hard in general. We propose a…
A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems
Jörg H. Kappes, Bjoern Andres, Fred A. Hamprecht +10
Szeliski et al. published an influential study in 2006 on energy minimization methods for Markov Random Fields (MRF). This study provided valuable insights in choosing the best opt…
Higher-order Segmentation via Multicuts
Joerg Hendrik Kappes, Markus Speth, Gerhard Reinelt +1
Multicuts enable to conveniently represent discrete graphical models for unsupervised and supervised image segmentation, in the case of local energy functions that exhibit symmetri…
Efficient MRF Energy Minimization via Adaptive Diminishing Smoothing
Bogdan Savchynskyy, Stefan Schmidt, Joerg Kappes +1
We consider the linear programming relaxation of an energy minimization problem for Markov Random Fields. The dual objective of this problem can be treated as a concave and unconst…
OpenGM: A C++ Library for Discrete Graphical Models
Bjoern Andres, Thorsten Beier, Joerg H. Kappes
OpenGM is a C++ template library for defining discrete graphical models and performing inference on these models, using a wide range of state-of-the-art algorithms. No restrictions…