A Comparative Study of Graph Matching Algorithms in Computer Vision

Benchmark Results for “motor”

This page shows the benchmarks results for the dataset “motor”. The reported values, bounds and accuracies are averaged across all instances of the dataset.

Run time 1s

method avg value avg bound feasible optimal accuracy
dd-ls0 -62.9452 -62.9751 20 / 20 20 / 20 97.35%
dd-ls3 -56.5448 -64.8838 20 / 20 13 / 20 86.94%
dd-ls4 -52.4233 -67.6857 20 / 20 6 / 20 77.65%
fgmd inf -inf 0 / 20 0 / 20
fm-bca -62.9452 -63.4077 20 / 20 20 / 20 97.35%
fm -62.7357 -93.584 20 / 20 18 / 20 93.37%
fw -57.898 -inf 20 / 20 4 / 20 69.57%
ga -61.9743 -inf 20 / 20 11 / 20 89.54%
hbp inf -inf 19 / 20 19 / 20
ipfps -60.558 -inf 20 / 20 5 / 20 84.80%
ipfpu -58.126 -inf 20 / 20 3 / 20 76.64%
lsm -51.9015 -inf 20 / 20 2 / 20 63.53%
mp -62.8405 -63.3573 20 / 20 18 / 20 96.25%
mp-fw -62.9361 -63.1621 20 / 20 19 / 20 97.74%
mpm inf -inf 18 / 20 1 / 20
mp-mcf -62.9088 -63.0906 20 / 20 18 / 20 97.80%
pm -35.1333 -inf 20 / 20 0 / 20 31.62%
rrwm -62.0936 -inf 20 / 20 10 / 20 88.98%
sm -59.8937 -inf 20 / 20 8 / 20 86.84%
smac -52.4058 -inf 20 / 20 2 / 20 65.80%

Run time 10s

method avg value avg bound feasible optimal accuracy
dd-ls0 -62.9452 -62.9751 20 / 20 20 / 20 97.35%
dd-ls3 -62.9452 -62.9556 20 / 20 20 / 20 97.35%
dd-ls4 -59.8436 -64.1404 20 / 20 14 / 20 92.59%
fgmd -62.8873 -inf 20 / 20 17 / 20 96.63%
fm-bca -62.9452 -63.4062 20 / 20 20 / 20 97.35%
fm -62.9452 -93.584 20 / 20 20 / 20 97.35%
fw -57.898 -inf 20 / 20 4 / 20 69.57%
ga -61.9743 -inf 20 / 20 11 / 20 89.54%
hbp -62.9452 -64.6303 20 / 20 20 / 20 97.35%
ipfps -60.558 -inf 20 / 20 5 / 20 84.80%
ipfpu -58.126 -inf 20 / 20 3 / 20 76.64%
lsm -51.9015 -inf 20 / 20 2 / 20 63.53%
mp -62.9361 -63.3401 20 / 20 19 / 20 97.74%
mp-fw -62.9452 -62.9952 20 / 20 20 / 20 97.35%
mpm -50.011 -inf 20 / 20 1 / 20 55.76%
mp-mcf -62.9409 -62.9869 20 / 20 19 / 20 98.70%
pm -35.1333 -inf 20 / 20 0 / 20 31.62%
rrwm -62.0936 -inf 20 / 20 10 / 20 88.98%
sm -59.8937 -inf 20 / 20 8 / 20 86.84%
smac -52.4058 -inf 20 / 20 2 / 20 65.80%

Run time 100s

method avg value avg bound feasible optimal accuracy
dd-ls0 -62.9452 -62.9751 20 / 20 20 / 20 97.35%
dd-ls3 -62.9452 -62.9555 20 / 20 20 / 20 97.35%
dd-ls4 -62.9452 -62.9493 20 / 20 20 / 20 97.35%
fgmd -62.8873 -inf 20 / 20 17 / 20 96.63%
fm-bca -62.9452 -63.4062 20 / 20 20 / 20 97.35%
fm -62.9452 -93.584 20 / 20 20 / 20 97.35%
fw -57.898 -inf 20 / 20 4 / 20 69.57%
ga -61.9743 -inf 20 / 20 11 / 20 89.54%
hbp -62.9452 -64.6303 20 / 20 20 / 20 97.35%
ipfps -60.558 -inf 20 / 20 5 / 20 84.80%
ipfpu -58.126 -inf 20 / 20 3 / 20 76.64%
lsm -51.9015 -inf 20 / 20 2 / 20 63.53%
mp -62.9361 -63.34 20 / 20 19 / 20 97.74%
mp-fw -62.9452 -62.949 20 / 20 20 / 20 97.35%
mpm -50.011 -inf 20 / 20 1 / 20 55.76%
mp-mcf -62.9409 -62.9499 20 / 20 19 / 20 98.70%
pm -35.1333 -inf 20 / 20 0 / 20 31.62%
rrwm -62.0936 -inf 20 / 20 10 / 20 88.98%
sm -59.8937 -inf 20 / 20 8 / 20 86.84%
smac -52.4058 -inf 20 / 20 2 / 20 65.80%

Run time 300s

method avg value avg bound feasible optimal accuracy
dd-ls0 -62.9452 -62.9751 20 / 20 20 / 20 97.35%
dd-ls3 -62.9452 -62.9555 20 / 20 20 / 20 97.35%
dd-ls4 -62.9452 -62.949 20 / 20 20 / 20 97.35%
fgmd -62.8873 -inf 20 / 20 17 / 20 96.63%
fm-bca -62.9452 -63.4062 20 / 20 20 / 20 97.35%
fm -62.9452 -93.584 20 / 20 20 / 20 97.35%
fw -57.898 -inf 20 / 20 4 / 20 69.57%
ga -61.9743 -inf 20 / 20 11 / 20 89.54%
hbp -62.9452 -64.6303 20 / 20 20 / 20 97.35%
ipfps -60.558 -inf 20 / 20 5 / 20 84.80%
ipfpu -58.126 -inf 20 / 20 3 / 20 76.64%
lsm -51.9015 -inf 20 / 20 2 / 20 63.53%
mp -62.9361 -63.34 20 / 20 19 / 20 97.74%
mp-fw -62.9452 -62.9489 20 / 20 20 / 20 97.35%
mpm -50.011 -inf 20 / 20 1 / 20 55.76%
mp-mcf -62.9409 -62.9459 20 / 20 19 / 20 98.70%
pm -35.1333 -inf 20 / 20 0 / 20 31.62%
rrwm -62.0936 -inf 20 / 20 10 / 20 88.98%
sm -59.8937 -inf 20 / 20 8 / 20 86.84%
smac -52.4058 -inf 20 / 20 2 / 20 65.80%

Per Instance Results

Results for individual instances of the dataset are also available: