  Most existing methods for object segmentation in computer vision are
formulated as a labeling task. This, in general, could be transferred to a
pixel-wise label assignment task, which is quite similar to the structure of
hidden Markov random field. In terms of Markov random field, each pixel can be
regarded as a state and has a transition probability to its neighbor pixel, the
label behind each pixel is a latent variable and has an emission probability
from its corresponding state. In this paper, we reviewed several modern image
labeling methods based on Markov random field and conditional random Field. And
we compare the result of these methods with some classical image labeling
methods. The experiment demonstrates that the introduction of Markov random
field and conditional random field make a big difference in the segmentation
result.
