A Comparative Study of Graph Matching Algorithms in Computer Vision

Benchmark Results for “car5”

This page shows the benchmarks results for the dataset instance “car5”. We consider solutions as optimal if the objective value is within a 0.1% range of the known optimum -66.25.

Run time 1s

method value bound optimal accuracy
dd-ls0 -66.2497 -66.2499 yes 100.00%
dd-ls3 -36.9086 -88.5964 no 15.38%
dd-ls4 -36.5291 -89.2372 no 23.08%
fgmd inf -inf no
fm-bca -66.2497 -74.9589 yes 100.00%
fm -58.2706 -123.852 no 30.77%
fw -52.1543 -inf no 0.00%
ga -57.4366 -inf no 0.00%
hbp inf -inf no
ipfps -54.6844 -inf no 64.10%
ipfpu -47.8303 -inf no 53.85%
lsm -35.4696 -inf no 7.69%
mp -63.5572 -74.3582 no 87.18%
mp-fw -65.2622 -74.7899 no 84.62%
mpm -37.6364 -inf no 0.00%
mp-mcf -63.3312 -73.8297 no 87.18%
pm -32.3504 -inf no 12.82%
rrwm -63.782 -inf no 69.23%
sm -54.4825 -inf no 66.67%
smac -45.7337 -inf no 28.21%

Run time 10s

method value bound optimal accuracy
dd-ls0 -66.2497 -66.2499 yes 100.00%
dd-ls3 -42.0561 -69.7971 no 48.72%
dd-ls4 -36.5291 -85.2936 no 23.08%
fgmd -66.2497 -inf yes 100.00%
fm-bca -66.2497 -74.93 yes 100.00%
fm -58.8259 -123.852 no 23.08%
fw -52.1543 -inf no 0.00%
ga -57.4366 -inf no 0.00%
hbp inf -inf no
ipfps -54.6844 -inf no 64.10%
ipfpu -47.8303 -inf no 53.85%
lsm -35.4696 -inf no 7.69%
mp -65.8243 -74.3559 no 94.87%
mp-fw -66.2497 -72.8427 yes 100.00%
mpm -37.6364 -inf no 0.00%
mp-mcf -64.3647 -71.7747 no 94.87%
pm -32.3504 -inf no 12.82%
rrwm -63.782 -inf no 69.23%
sm -54.4825 -inf no 66.67%
smac -45.7337 -inf no 28.21%

Run time 100s

method value bound optimal accuracy
dd-ls0 -66.2497 -66.2499 yes 100.00%
dd-ls3 -66.2497 -66.2498 yes 100.00%
dd-ls4 -42.1041 -70.5553 no 48.72%
fgmd -66.2497 -inf yes 100.00%
fm-bca -66.2497 -74.93 yes 100.00%
fm -58.8259 -123.852 no 23.08%
fw -52.1543 -inf no 0.00%
ga -57.4366 -inf no 0.00%
hbp -65.142 -76.8618 no 92.31%
ipfps -54.6844 -inf no 64.10%
ipfpu -47.8303 -inf no 53.85%
lsm -35.4696 -inf no 7.69%
mp -66.2497 -74.3559 yes 100.00%
mp-fw -66.2497 -67.8972 yes 100.00%
mpm -37.6364 -inf no 0.00%
mp-mcf -66.2497 -67.5775 yes 100.00%
pm -32.3504 -inf no 12.82%
rrwm -63.782 -inf no 69.23%
sm -54.4825 -inf no 66.67%
smac -45.7337 -inf no 28.21%

Run time 300s

method value bound optimal accuracy
dd-ls0 -66.2497 -66.2499 yes 100.00%
dd-ls3 -66.2497 -66.2498 yes 100.00%
dd-ls4 -66.2497 -66.2498 yes 100.00%
fgmd -66.2497 -inf yes 100.00%
fm-bca -66.2497 -74.93 yes 100.00%
fm -58.8259 -123.852 no 23.08%
fw -52.1543 -inf no 0.00%
ga -57.4366 -inf no 0.00%
hbp -65.142 -76.8618 no 92.31%
ipfps -54.6844 -inf no 64.10%
ipfpu -47.8303 -inf no 53.85%
lsm -35.4696 -inf no 7.69%
mp -66.2497 -74.3559 yes 100.00%
mp-fw -66.2497 -67.3294 yes 100.00%
mpm -37.6364 -inf no 0.00%
mp-mcf -66.2497 -67.2987 yes 100.00%
pm -32.3504 -inf no 12.82%
rrwm -63.782 -inf no 69.23%
sm -54.4825 -inf no 66.67%
smac -45.7337 -inf no 28.21%

Other Results for this Dataset

Accumulated results for whole dataset: car

Results for individual instances of the dataset: