A Comparative Study of Graph Matching Algorithms in Computer Vision

Benchmark Results for “car30”

This page shows the benchmarks results for the dataset instance “car30”. We consider solutions as optimal if the objective value is within a 0.1% range of the known optimum -58.85.

Run time 1s

method value bound optimal accuracy
dd-ls0 -58.8512 -58.8583 yes 78.57%
dd-ls3 -58.8512 -59.7362 yes 78.57%
dd-ls4 -46.1307 -65.9633 no 60.71%
fgmd inf -inf no
fm-bca -58.8512 -59.4922 yes 78.57%
fm -58.8512 -91.8961 yes 78.57%
fw -56.7091 -inf no 64.29%
ga -58.8512 -inf yes 78.57%
hbp -58.8512 -61.878 yes 78.57%
ipfps -56.1418 -inf no 85.71%
ipfpu -47.9635 -inf no 67.86%
lsm -50.1355 -inf no 64.29%
mp -58.4318 -59.3628 no 75.00%
mp-fw -58.8512 -59.1124 yes 78.57%
mpm -55.0032 -inf no 71.43%
mp-mcf -58.8512 -59.0643 yes 78.57%
pm -36.5149 -inf no 35.71%
rrwm -57.2955 -inf no 78.57%
sm -50.7051 -inf no 64.29%
smac -34.5866 -inf no 21.43%

Run time 10s

method value bound optimal accuracy
dd-ls0 -58.8512 -58.8583 yes 78.57%
dd-ls3 -58.8512 -58.8543 yes 78.57%
dd-ls4 -58.8512 -59.1516 yes 78.57%
fgmd -58.8512 -inf yes 78.57%
fm-bca -58.8512 -59.4922 yes 78.57%
fm -58.8512 -91.8961 yes 78.57%
fw -56.7091 -inf no 64.29%
ga -58.8512 -inf yes 78.57%
hbp -58.8512 -61.878 yes 78.57%
ipfps -56.1418 -inf no 85.71%
ipfpu -47.9635 -inf no 67.86%
lsm -50.1355 -inf no 64.29%
mp -58.8512 -59.3628 yes 78.57%
mp-fw -58.8512 -59.0103 yes 78.57%
mpm -55.0032 -inf no 71.43%
mp-mcf -58.8512 -58.9245 yes 78.57%
pm -36.5149 -inf no 35.71%
rrwm -57.2955 -inf no 78.57%
sm -50.7051 -inf no 64.29%
smac -34.5866 -inf no 21.43%

Run time 100s

method value bound optimal accuracy
dd-ls0 -58.8512 -58.8583 yes 78.57%
dd-ls3 -58.8512 -58.8543 yes 78.57%
dd-ls4 -58.8512 -58.8517 yes 78.57%
fgmd -58.8512 -inf yes 78.57%
fm-bca -58.8512 -59.4922 yes 78.57%
fm -58.8512 -91.8961 yes 78.57%
fw -56.7091 -inf no 64.29%
ga -58.8512 -inf yes 78.57%
hbp -58.8512 -61.878 yes 78.57%
ipfps -56.1418 -inf no 85.71%
ipfpu -47.9635 -inf no 67.86%
lsm -50.1355 -inf no 64.29%
mp -58.8512 -59.3628 yes 78.57%
mp-fw -58.8512 -58.8512 yes 78.57%
mpm -55.0032 -inf no 71.43%
mp-mcf -58.8512 -58.8512 yes 78.57%
pm -36.5149 -inf no 35.71%
rrwm -57.2955 -inf no 78.57%
sm -50.7051 -inf no 64.29%
smac -34.5866 -inf no 21.43%

Run time 300s

method value bound optimal accuracy
dd-ls0 -58.8512 -58.8583 yes 78.57%
dd-ls3 -58.8512 -58.8543 yes 78.57%
dd-ls4 -58.8512 -58.8517 yes 78.57%
fgmd -58.8512 -inf yes 78.57%
fm-bca -58.8512 -59.4922 yes 78.57%
fm -58.8512 -91.8961 yes 78.57%
fw -56.7091 -inf no 64.29%
ga -58.8512 -inf yes 78.57%
hbp -58.8512 -61.878 yes 78.57%
ipfps -56.1418 -inf no 85.71%
ipfpu -47.9635 -inf no 67.86%
lsm -50.1355 -inf no 64.29%
mp -58.8512 -59.3628 yes 78.57%
mp-fw -58.8512 -58.8512 yes 78.57%
mpm -55.0032 -inf no 71.43%
mp-mcf -58.8512 -58.8512 yes 78.57%
pm -36.5149 -inf no 35.71%
rrwm -57.2955 -inf no 78.57%
sm -50.7051 -inf no 64.29%
smac -34.5866 -inf no 21.43%

Other Results for this Dataset

Accumulated results for whole dataset: car

Results for individual instances of the dataset: