Title :
Hidden Conditional Random Fields
Author :
Quattoni, Ariadna ; Wang, Sybor ; Morency, Louis-Philippe ; Collins, Michael ; Darrell, Trevor
Author_Institution :
Massachusetts Inst. of Technol., Cambridge
Abstract :
We present a discriminative latent variable model for classification problems in structured domains where inputs can be represented by a graph of local observations. A hidden-state conditional random field framework learns a set of latent variables conditioned on local features. Observations need not be independent and may overlap in space and time.
Keywords :
graph theory; learning (artificial intelligence); discriminative latent variable model; hidden-state conditional random field framework; supervised learning; Bayesian methods; Graphical models; Handicapped aids; Hidden Markov models; Inference algorithms; Labeling; Natural language processing; Object recognition; Parameter estimation; Supervised learning; classification; model; object recognition; supervised learning; Algorithms; Artificial Intelligence; Computer Simulation; Data Interpretation, Statistical; Image Enhancement; Image Interpretation, Computer-Assisted; Markov Chains; Models, Statistical; Pattern Recognition, Automated; Reproducibility of Results; Sensitivity and Specificity;
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
DOI :
10.1109/TPAMI.2007.1124
