Apr 6, 2017

Why CRF?

MRF is a generative model. Hence we need to model
i) the likelihood of image given label
ii) prior of label
the inference can be modeled from the joint probability (using Bayes theorm)  as a conditional probability of label given the image.  
To make the inference tractable only local relationship between labels are encoded into in the form ii).

CRF can directly model the conditional probability of label given image, hence we don't need to explicitly model i) and ii).

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