Paper Info
Title
Fast Approximate Energy Minimization with Label Costs
Abstract
The 驴-expansion algorithm has had a significant impact in computer vision due to its generality, effectiveness, and speed. It is commonly used to minimize energies that involve unary, pairwise, and specialized higher-order terms. Our main algorithmic contribution is an extension of 驴-expansion that also optimizes "label costs" with well-characterized optimality bounds. Label costs penalize a solution based on the set of labels that appear in it, for example by simply penalizing the number of labels in the solution.Our energy has a natural interpretation as minimizing description length (MDL) and sheds light on classical algorithms like K-means and expectation-maximization (EM). Label costs are useful for multi-model fitting and we demonstrate several such applications: homography detection, motion segmentation, image segmentation, and compression. Our C++ and MATLAB code is publicly available http://vision.csd.uwo.ca/code/ .
Year
DOI
Venue
2010
10.1007/s11263-011-0437-z
International Journal of Computer Vision
Keywords
DocType
Volume
Energy minimization,Multi-model fitting,Metric labeling,Graph cuts,Minimum description length
Conference
96
Issue
ISSN
Citations 
1
0920-5691
229
PageRank 
References 
Authors
6.16
47
4
Search Limit
100229
Name
Order
Citations
PageRank
Andrew Delong144817.35
A. Osokin243019.01
Hossam N. Isack33069.93
Yuri Boykov47601497.20