Journal
MATHEMATICAL PROBLEMS IN ENGINEERING
Volume 2016, Issue -, Pages -Publisher
HINDAWI LTD
DOI: 10.1155/2016/3846125
Keywords
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Funding
- Special Fund Project of Industry-Education-Academy Cooperation in Guangdong Province [2013A090100002]
- National High Technology Research and Development Program(863 Program) of China [2015AA043302]
- Key Scientific Projects of Guangzhou Huadu [HD14ZD004]
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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.
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