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

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

BT,BnB

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

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.

Results

Results Iterations

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.

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

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