Optimization methods for discriminative training of conditional random fields based on minimum tag error

Author(s):  
Y. Xiong
2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
Tong Liu ◽  
Xiutian Huang ◽  
Jianshe Ma

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.


2009 ◽  
Vol 179 (1-2) ◽  
pp. 169-179 ◽  
Author(s):  
Ying Xiong ◽  
Jie Zhu ◽  
Hao Huang ◽  
Haihua Xu

2011 ◽  
Vol 22 (8) ◽  
pp. 1897-1910 ◽  
Author(s):  
Yun LIU ◽  
Zhi-Ping CAI ◽  
Ping ZHONG ◽  
Jian-Ping YIN ◽  
Jie-Ren CHENG

ROBOT ◽  
2010 ◽  
Vol 32 (3) ◽  
pp. 326-333
Author(s):  
Mingjun WANG ◽  
Jun ZHOU ◽  
Jun TU ◽  
Chengliang LIU

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