Paper Info
Title
An empirical comparison of inference algorithms for graphical models with higher order factors using openGM
Abstract
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.
Year
DOI
Venue
2010
10.1007/978-3-642-15986-2_36
DAGM-Symposium
Keywords
Field
DocType
empirical comparison,inference algorithm,higher order factor,computer vision,higher order model,considerable attention,real-world graphical model,important tool,graphical model,order factor,pattern recognition,higher order,second order
Empirical comparison,Software design,Computer science,Inference,Algorithm,Artificial intelligence,Graphical model,Machine learning
Conference
Volume
ISSN
ISBN
6376
0302-9743
3-642-15985-0
Citations 
PageRank 
References 
16
0.82
21
Authors
5
Name
Order
Citations
PageRank
Björn Andres122012.72
Jörg H. Kappes220212.16
Ullrich Koethe324922.37
Christoph Schnörr43025219.34
Fred A. Hamprecht596276.24