Conditional Random Fields for Image Labeling

被引：6

作者：

Liu, Tong ^{[1
]}

Huang, Xiutian ^{[1
]}

Ma, Jianshe ^{[1
]}

机构：

[1] Tsinghua Univ, Grad Sch Shenzhen, Div Adv Mfg, Shenzhen 518055, Peoples R China

来源：

MATHEMATICAL PROBLEMS IN ENGINEERING | 2016年 / 2016卷

关键词：

ENERGY MINIMIZATION; GRAPH CUTS; SEGMENTATION; OBJECTS; MODEL;

D O I：

10.1155/2016/3846125

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

With the rapid development and application of CRFs (Conditional Random Fields) in computer vision, many researchers have made some outstanding progress in this domain because CRFs solve the classical version of the label bias problem with respect to MEMMs (maximum entropy Markov models) and HMMs (hidden Markov models). This paper reviews the research development and status of object recognition with CRFs and especially introduces two main discrete optimization methods for image labeling with CRFs: graph cut and mean field approximation. This paper describes graph cut briefly while it introduces mean field approximation more detailedly which has a substantial speed of inference and is researched popularly in recent years.

引用

页数：15

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