A Comparative Study of Graph Matching Algorithms in Computer Vision

Benchmark Results for “car4”

This page shows the benchmarks results for the dataset instance “car4”. We consider solutions as optimal if the objective value is within a 0.1% range of the known optimum -52.34.

Run time 1s

method value bound optimal accuracy
dd-ls0 -51.2538 -53.5633 no 36.36%
dd-ls3 -32.5875 -67.9763 no 21.21%
dd-ls4 -36.5547 -72.7252 no 24.24%
fgmd inf -inf no
fm-bca -52.2327 -60.3678 no 42.42%
fm -51.2125 -105.248 no 42.42%
fw -46.5002 -inf no 33.33%
ga -50.9443 -inf no 24.24%
hbp inf -inf no
ipfps -46.3394 -inf no 45.45%
ipfpu -40.9584 -inf no 36.36%
lsm -36.4803 -inf no 27.27%
mp -49.5478 -60.0128 no 33.33%
mp-fw -51.6697 -61.1753 no 27.27%
mpm -39.828 -inf no 24.24%
mp-mcf -50.3371 -60.2142 no 36.36%
pm -31.9153 -inf no 15.15%
rrwm -48.1492 -inf no 66.67%
sm -44.2688 -inf no 48.48%
smac -37.5042 -inf no 48.48%

Run time 10s

method value bound optimal accuracy
dd-ls0 -51.2538 -53.5499 no 36.36%
dd-ls3 -49.5774 -53.5233 no 45.45%
dd-ls4 -36.5547 -63.5772 no 24.24%
fgmd -51.772 -inf no 24.24%
fm-bca -52.3307 -60.3671 yes 45.45%
fm -51.2125 -105.248 no 42.42%
fw -46.5002 -inf no 33.33%
ga -50.9443 -inf no 24.24%
hbp -49.8959 -64.9764 no 33.33%
ipfps -46.3394 -inf no 45.45%
ipfpu -40.9584 -inf no 36.36%
lsm -36.4803 -inf no 27.27%
mp -49.5478 -60.0117 no 33.33%
mp-fw -52.3356 -59.5356 yes 42.42%
mpm -39.828 -inf no 24.24%
mp-mcf -50.3371 -57.4626 no 36.36%
pm -31.9153 -inf no 15.15%
rrwm -48.1492 -inf no 66.67%
sm -44.2688 -inf no 48.48%
smac -37.5042 -inf no 48.48%

Run time 100s

method value bound optimal accuracy
dd-ls0 -51.2538 -53.5499 no 36.36%
dd-ls3 -52.0871 -53.1912 no 36.36%
dd-ls4 -51.2103 -53.2785 no 39.39%
fgmd -51.772 -inf no 24.24%
fm-bca -52.3356 -60.3671 yes 42.42%
fm -51.2125 -105.248 no 42.42%
fw -46.5002 -inf no 33.33%
ga -50.9443 -inf no 24.24%
hbp -49.8959 -64.9764 no 33.33%
ipfps -46.3394 -inf no 45.45%
ipfpu -40.9584 -inf no 36.36%
lsm -36.4803 -inf no 27.27%
mp -49.5478 -60.0117 no 33.33%
mp-fw -52.3356 -55.227 yes 42.42%
mpm -39.828 -inf no 24.24%
mp-mcf -50.3371 -54.9451 no 36.36%
pm -31.9153 -inf no 15.15%
rrwm -48.1492 -inf no 66.67%
sm -44.2688 -inf no 48.48%
smac -37.5042 -inf no 48.48%

Run time 300s

method value bound optimal accuracy
dd-ls0 -51.2538 -53.5499 no 36.36%
dd-ls3 -52.0871 -53.1912 no 36.36%
dd-ls4 -52.3307 -52.8454 yes 45.45%
fgmd -51.772 -inf no 24.24%
fm-bca -52.3356 -60.3671 yes 42.42%
fm -51.2125 -105.248 no 42.42%
fw -46.5002 -inf no 33.33%
ga -50.9443 -inf no 24.24%
hbp -49.8959 -64.9764 no 33.33%
ipfps -46.3394 -inf no 45.45%
ipfpu -40.9584 -inf no 36.36%
lsm -36.4803 -inf no 27.27%
mp -49.5478 -60.0117 no 33.33%
mp-fw -52.3356 -54.814 yes 42.42%
mpm -39.828 -inf no 24.24%
mp-mcf -50.3371 -54.7429 no 36.36%
pm -31.9153 -inf no 15.15%
rrwm -48.1492 -inf no 66.67%
sm -44.2688 -inf no 48.48%
smac -37.5042 -inf no 48.48%

Other Results for this Dataset

Accumulated results for whole dataset: car

Results for individual instances of the dataset: