Conditional random field (CRF) 

A conditional random field (CRF) is a statistical modelling method often applied in pattern recognition. More specifically it is a type of discriminative undirected probabilistic graphical model. It is used to encode known relationships between observations and construct consistent interpretations. It is often used for labeling or parsing of sequential data, such as natural language text or biological sequences and in computer vision. Specifically, CRFs find applications in shallow parsing, named entity recognition and gene finding, among other tasks, being an alternative to the related hidden Markov models. In computer vision, CRFs are often used for object recognition and image segmentation. The scoring functions of a CRF are given as arbitrary potential functions and CRFs are trained via conditional maximum likelihood estimation (CML). GCRF is a generalized CRF in which feature length can be explicitely modeled (Wikipedia, Edwards 2009)