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