Fast Approximate Energy Minimization with Label Costs

被引：54

作者：

Delong, Andrew ^{[1
]}

Osokin, Anton ^{[2
]}

Isack, Hossam N. ^{[1
]}

Boykov, Yuri ^{[1
]}

机构：

[1] Univ Western Ontario, Dept Comp Sci, London, ON N6A 3K7, Canada

[2] Moscow MV Lomonosov State Univ, Dept Comp Math & Cybernetics, Moscow 117234, Russia

来源：

2010 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR) | 2010年

基金：

加拿大自然科学与工程研究理事会;

关键词：

D O I：

10.1109/CVPR.2010.5539897

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The alpha-expansion algorithm [4] has had a significant impact in computer vision due to its generality, effectiveness, and speed. Thus far it can only minimize energies that involve unary, pairwise, and specialized higher-order terms. Our main contribution is to extend alpha-expansion so that it can simultaneously optimize "label costs" as well. An energy with label costs can penalize a solution based on the set of labels that appear in it. The simplest special case is to penalize the number of labels in the solution. Our energy is quite general, and we prove optimality bounds for our algorithm. A natural application of label costs is multi-model fitting, and we demonstrate several such applications in vision: homography detection, motion segmentation, and unsupervised image segmentation. Our C++/MATLAB implementation is publicly available.

引用

页码：2173 / 2180

页数：8

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