Hit the following command to execute the algorithm.
python main.py [OPTIONS]
OPTIONS:
--segments INTEGER The number of segments you want to segment your image (e.g. 4, 8, 16...), default=8
--path TEXT The file path to your image. (e.g. im.jpg), default=im.jpg
--iterations INTEGER The number of iterations you want to run the algorithm, default=100
--tolerance FLOAT The tolerance you accept, default=1e-6
- matplotlib==3.0.2
- Click==7.0
We have the above mixture distribution:
We want to maximize the log Likelihood:
We have to construct the Complete likelihood:
And then the Complete logLikelihood is defined as:
But we don't know the value of the hidden variable z each time, so we must compute the Expected Complete likelihood.
Since each appears linearly in
the mean value of
is obtained by substituting the
with their mean value :
where
In order to get the equations of the Maximization Step, we differentiate the above function with respect to and
each time and assuming that
are constant (in terms of
and
) since they have been calculated in the Expectation Step based on the previous parameter values.
After that we set the differentiation equals to 0 :
On this step we need to add the LaGrange Multiplier in order to find the exact formula.
And last one:
