Bayes rule
In terms of our problem:
what we measure
(
likelihood
)
domain knowledge
(
prior
)
what we want
(
posterior
)
normalization
term
The prior:
P(skin)
•
Could use domain knowledge
–
P(skin) may be larger if we know the image contains a person
–
for a portrait, P(skin) may be higher for pixels in the center
•
Could learn the prior from the training set.
How?
–
P(skin) may be proportion of skin pixels in training set
