A Comparative Study of Graph Matching Algorithms in Computer Vision

Benchmark Results for “car3”

This page shows the benchmarks results for the dataset instance “car3”. We consider solutions as optimal if the objective value is within a 0.1% range of the known optimum -86.55.

Run time 1s

method value bound optimal accuracy
dd-ls0 -86.5484 -86.5517 yes 100.00%
dd-ls3 -49.5535 -102.721 no 33.33%
dd-ls4 -52.1307 -104.004 no 38.10%
fgmd inf -inf no
fm-bca -86.5484 -87.796 yes 100.00%
fm -86.3392 -141.38 no 90.48%
fw -82.7882 -inf no 76.19%
ga -85.8752 -inf no 83.33%
hbp inf -inf no
ipfps -85.3996 -inf no 85.71%
ipfpu -78.6194 -inf no 76.19%
lsm -68.6243 -inf no 59.52%
mp -86.5484 -87.7693 yes 100.00%
mp-fw -86.5484 -88.2131 yes 100.00%
mpm inf -inf no
mp-mcf -86.5484 -87.4724 yes 100.00%
pm -43.4407 -inf no 23.81%
rrwm -86.3392 -inf no 90.48%
sm -76.6988 -inf no 59.52%
smac -75.7614 -inf no 71.43%

Run time 10s

method value bound optimal accuracy
dd-ls0 -86.5484 -86.5517 yes 100.00%
dd-ls3 -86.5484 -86.9853 yes 100.00%
dd-ls4 -52.1307 -97.2985 no 38.10%
fgmd -86.3392 -inf no 90.48%
fm-bca -86.5484 -87.7558 yes 100.00%
fm -86.5484 -141.38 yes 100.00%
fw -82.7882 -inf no 76.19%
ga -85.8752 -inf no 83.33%
hbp -86.5484 -92.094 yes 100.00%
ipfps -85.3996 -inf no 85.71%
ipfpu -78.6194 -inf no 76.19%
lsm -68.6243 -inf no 59.52%
mp -86.5484 -87.7476 yes 100.00%
mp-fw -86.5484 -86.9275 yes 100.00%
mpm -70.0829 -inf no 54.76%
mp-mcf -86.5484 -86.9038 yes 100.00%
pm -43.4407 -inf no 23.81%
rrwm -86.3392 -inf no 90.48%
sm -76.6988 -inf no 59.52%
smac -75.7614 -inf no 71.43%

Run time 100s

method value bound optimal accuracy
dd-ls0 -86.5484 -86.5517 yes 100.00%
dd-ls3 -86.5484 -86.5534 yes 100.00%
dd-ls4 -86.5484 -86.5692 yes 100.00%
fgmd -86.3392 -inf no 90.48%
fm-bca -86.5484 -87.7558 yes 100.00%
fm -86.5484 -141.38 yes 100.00%
fw -82.7882 -inf no 76.19%
ga -85.8752 -inf no 83.33%
hbp -86.5484 -92.094 yes 100.00%
ipfps -85.3996 -inf no 85.71%
ipfpu -78.6194 -inf no 76.19%
lsm -68.6243 -inf no 59.52%
mp -86.5484 -87.7476 yes 100.00%
mp-fw -86.5484 -86.9027 yes 100.00%
mpm -70.0829 -inf no 54.76%
mp-mcf -86.5484 -86.8341 yes 100.00%
pm -43.4407 -inf no 23.81%
rrwm -86.3392 -inf no 90.48%
sm -76.6988 -inf no 59.52%
smac -75.7614 -inf no 71.43%

Run time 300s

method value bound optimal accuracy
dd-ls0 -86.5484 -86.5517 yes 100.00%
dd-ls3 -86.5484 -86.5534 yes 100.00%
dd-ls4 -86.5484 -86.5493 yes 100.00%
fgmd -86.3392 -inf no 90.48%
fm-bca -86.5484 -87.7558 yes 100.00%
fm -86.5484 -141.38 yes 100.00%
fw -82.7882 -inf no 76.19%
ga -85.8752 -inf no 83.33%
hbp -86.5484 -92.094 yes 100.00%
ipfps -85.3996 -inf no 85.71%
ipfpu -78.6194 -inf no 76.19%
lsm -68.6243 -inf no 59.52%
mp -86.5484 -87.7476 yes 100.00%
mp-fw -86.5484 -86.6705 yes 100.00%
mpm -70.0829 -inf no 54.76%
mp-mcf -86.5484 -86.6531 yes 100.00%
pm -43.4407 -inf no 23.81%
rrwm -86.3392 -inf no 90.48%
sm -76.6988 -inf no 59.52%
smac -75.7614 -inf no 71.43%

Other Results for this Dataset

Accumulated results for whole dataset: car

Results for individual instances of the dataset: