A Comparative Study of Graph Matching Algorithms in Computer Vision

Benchmark Results for “house-sparse56”

This page shows the benchmarks results for the dataset instance “house-sparse56”. We consider solutions as optimal if the objective value is within a 0.1% range of the known optimum -66.5399.

Run time 1s

method value bound optimal accuracy
dd-ls0 -66.5399 -66.5466 yes 100.00%
dd-ls3 -66.5399 -66.5663 yes 100.00%
dd-ls4 -47.6366 -71.7723 no 80.00%
fgmd inf -inf no
fm-bca -66.5399 -66.5399 yes 100.00%
fm -66.5399 -78.8591 yes 100.00%
fw 0 -inf no 0.00%
ga -66.5399 -inf yes 100.00%
hbp -66.5399 -66.603 yes 100.00%
ipfps -66.5399 -inf yes 100.00%
ipfpu -66.5399 -inf yes 100.00%
lsm -66.5399 -inf yes 100.00%
mp -66.5399 -66.5399 yes 100.00%
mp-fw -66.5399 -66.5399 yes 100.00%
mpm -61.7794 -inf no 90.00%
mp-mcf -66.5399 -66.5399 yes 100.00%
pm -55.6428 -inf no 86.67%
rrwm -66.5399 -inf yes 100.00%
sm -66.5399 -inf yes 100.00%
smac -66.5399 -inf yes 100.00%

Run time 10s

method value bound optimal accuracy
dd-ls0 -66.5399 -66.5466 yes 100.00%
dd-ls3 -66.5399 -66.5663 yes 100.00%
dd-ls4 -66.5399 -66.5415 yes 100.00%
fgmd -66.5399 -inf yes 100.00%
fm-bca -66.5399 -66.5399 yes 100.00%
fm -66.5399 -78.8591 yes 100.00%
fw 0 -inf no 0.00%
ga -66.5399 -inf yes 100.00%
hbp -66.5399 -66.603 yes 100.00%
ipfps -66.5399 -inf yes 100.00%
ipfpu -66.5399 -inf yes 100.00%
lsm -66.5399 -inf yes 100.00%
mp -66.5399 -66.5399 yes 100.00%
mp-fw -66.5399 -66.5399 yes 100.00%
mpm -61.7794 -inf no 90.00%
mp-mcf -66.5399 -66.5399 yes 100.00%
pm -55.6428 -inf no 86.67%
rrwm -66.5399 -inf yes 100.00%
sm -66.5399 -inf yes 100.00%
smac -66.5399 -inf yes 100.00%

Run time 100s

method value bound optimal accuracy
dd-ls0 -66.5399 -66.5466 yes 100.00%
dd-ls3 -66.5399 -66.5663 yes 100.00%
dd-ls4 -66.5399 -66.5415 yes 100.00%
fgmd -66.5399 -inf yes 100.00%
fm-bca -66.5399 -66.5399 yes 100.00%
fm -66.5399 -78.8591 yes 100.00%
fw 0 -inf no 0.00%
ga -66.5399 -inf yes 100.00%
hbp -66.5399 -66.603 yes 100.00%
ipfps -66.5399 -inf yes 100.00%
ipfpu -66.5399 -inf yes 100.00%
lsm -66.5399 -inf yes 100.00%
mp -66.5399 -66.5399 yes 100.00%
mp-fw -66.5399 -66.5399 yes 100.00%
mpm -61.7794 -inf no 90.00%
mp-mcf -66.5399 -66.5399 yes 100.00%
pm -55.6428 -inf no 86.67%
rrwm -66.5399 -inf yes 100.00%
sm -66.5399 -inf yes 100.00%
smac -66.5399 -inf yes 100.00%

Run time 300s

method value bound optimal accuracy
dd-ls0 -66.5399 -66.5466 yes 100.00%
dd-ls3 -66.5399 -66.5663 yes 100.00%
dd-ls4 -66.5399 -66.5415 yes 100.00%
fgmd -66.5399 -inf yes 100.00%
fm-bca -66.5399 -66.5399 yes 100.00%
fm -66.5399 -78.8591 yes 100.00%
fw 0 -inf no 0.00%
ga -66.5399 -inf yes 100.00%
hbp -66.5399 -66.603 yes 100.00%
ipfps -66.5399 -inf yes 100.00%
ipfpu -66.5399 -inf yes 100.00%
lsm -66.5399 -inf yes 100.00%
mp -66.5399 -66.5399 yes 100.00%
mp-fw -66.5399 -66.5399 yes 100.00%
mpm -61.7794 -inf no 90.00%
mp-mcf -66.5399 -66.5399 yes 100.00%
pm -55.6428 -inf no 86.67%
rrwm -66.5399 -inf yes 100.00%
sm -66.5399 -inf yes 100.00%
smac -66.5399 -inf yes 100.00%

Other Results for this Dataset

Accumulated results for whole dataset: house-sparse

Results for individual instances of the dataset: