1. BnB vs Label Relaxation.By
Dima Vingurt
Report as part of final project at course Computational and Biological Vision

2. CSP, maxCSP,COP Constrain Matrix Random Problem Variables and values.
P1 –density: chance of constrain
P2-tightness:chance of pair.
M- max weight.

3. BT,BnB

4. Relaxation Labeling(RL)
Set of objects – variables
Labels –domain
P- initial p is random.
Compatibility – constrain matrix
Support:
Update rule:

5. Results Each problem have 10 random starts.
Each time : 10 and 50 iteration was done.
n=d=4
X-axis is p1=p2
Average deference between real price (BnB) and RL. Price for each RL problem is averaged for 10 runs.

6. Results

7. Results
Iterations

8. Conclusions RL is not complete algorithm.
RL - chip and wrong; BnB heavy and always right.
RL- may be by changing a star condition will improve the result.

9. Reference
Lecture notes “Computational and Biological Vision” by Ohad Ben-Shahar , 2014
Lecture notes “Constraints Processing ” by Amnon Meisels, 2014

10. Thank you for your attention.