Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
What Energy Functions Can Be Minimized via Graph Cuts?
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Convergent Tree-Reweighted Message Passing for Energy Minimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Higher Order Potentials for Enforcing Label Consistency
International Journal of Computer Vision
Graphical Models, Exponential Families, and Variational Inference
Graphical Models, Exponential Families, and Variational Inference
P³ & Beyond: Move Making Algorithms for Solving Higher Order Functions
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Study of Parts-Based Object Class Detection Using Complete Graphs
International Journal of Computer Vision
Exact solution of permuted submodular minsum problems
EMMCVPR'07 Proceedings of the 6th international conference on Energy minimization methods in computer vision and pattern recognition
Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning
libDAI: A Free and Open Source C++ Library for Discrete Approximate Inference in Graphical Models
The Journal of Machine Learning Research
Loopy belief propagation for approximate inference: an empirical study
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Comparison of energy minimization algorithms for highly connected graphs
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
The generalized distributive law
IEEE Transactions on Information Theory
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
On the optimality of solutions of the max-product belief-propagation algorithm in arbitrary graphs
IEEE Transactions on Information Theory
A discrete chain graph model for 3d+t cell tracking with high misdetection robustness
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
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Graphical models with higher order factors are an important tool for pattern recognition that has recently attracted considerable attention. Inference based on such models is challenging both from the view point of software design and optimization theory. In this article, we use the new C++ template library OpenGM to empirically compare inference algorithms on a set of synthetic and real-world graphical models with higher order factors that are used in computer vision. While inference algorithms have been studied intensively for graphical models with second order factors, an empirical comparison for higher order models has so far been missing. This article presents a first set of experiments that intends to fill this gap.