1. Segmentation Using Metropolis Algorithm

2. Segmentation
Identifying objects in a picture by attaching each pixel to a specific segment
Pixels are characterized by different parameters: Place, RGB, gradient, etc.

3. The Metropolis Algorithm
Finds global minimum in any function
Based on Thermodynamics basic assumption:
while not finished
if energy isn't changed for the 5th time
finish
else
choose a random point P_new and compare to current point P_current
the distance to the new point is proportional to T/Tmax
if E(P_new) < E(P_current)
switch to the new point
if E(P_new) < E(P_best)
choose P_new to be P_best
choose a random number m
if m < exp(-(E(P_new) - E(P_current))/T)
if this is the 20'th iteration
decrease T by 10 percent

4. Implementation
Energy calculation is based on the sum of min distances of each pixel from its center
Distance is in 11-dimensional space: x, y, RGB, gradient
Compared against Segmentation Using Clustering Algorithm
Comparison based on number of indexing

5. Poor Results
Original
Metropolis
Clustering

7. Conclusion - Metropolis
Not better than any algorithm
Exact centers are less important than converging
Calibration of Metropolis is not clear

8. Conclusion - Segmentation
Number of segments is not clear
Energy function is not clear
Parameters to search are not clear