A Comparative Study of Graph Matching Algorithms in Computer Vision

Benchmark Results for “house-sparse59”

This page shows the benchmarks results for the dataset instance “house-sparse59”. We consider solutions as optimal if the objective value is within a 0.1% range of the known optimum -65.3659.

Run time 1s

method value bound optimal accuracy
dd-ls0 -65.3659 -65.3772 yes 100.00%
dd-ls3 -65.3659 -67.4442 yes 100.00%
dd-ls4 -48.9741 -72.2873 no 76.67%
fgmd inf -inf no
fm-bca -65.3659 -65.3659 yes 100.00%
fm -65.3659 -78.8203 yes 100.00%
fw 0 -inf no 0.00%
ga -65.3659 -inf yes 100.00%
hbp -65.3659 -65.9592 yes 100.00%
ipfps -65.3659 -inf yes 100.00%
ipfpu -65.3659 -inf yes 100.00%
lsm -62.1667 -inf no 93.33%
mp -65.3659 -65.3659 yes 100.00%
mp-fw -65.3659 -65.3659 yes 100.00%
mpm -57.3665 -inf no 83.33%
mp-mcf -65.3659 -65.3659 yes 100.00%
pm -52.6047 -inf no 80.00%
rrwm -65.3659 -inf yes 100.00%
sm -65.3659 -inf yes 100.00%
smac -44.4311 -inf no 66.67%

Run time 10s

method value bound optimal accuracy
dd-ls0 -65.3659 -65.3772 yes 100.00%
dd-ls3 -65.3659 -65.3846 yes 100.00%
dd-ls4 -65.3659 -65.3663 yes 100.00%
fgmd -65.3659 -inf yes 100.00%
fm-bca -65.3659 -65.3659 yes 100.00%
fm -65.3659 -78.8203 yes 100.00%
fw 0 -inf no 0.00%
ga -65.3659 -inf yes 100.00%
hbp -65.3659 -65.9592 yes 100.00%
ipfps -65.3659 -inf yes 100.00%
ipfpu -65.3659 -inf yes 100.00%
lsm -62.1667 -inf no 93.33%
mp -65.3659 -65.3659 yes 100.00%
mp-fw -65.3659 -65.3659 yes 100.00%
mpm -57.3665 -inf no 83.33%
mp-mcf -65.3659 -65.3659 yes 100.00%
pm -52.6047 -inf no 80.00%
rrwm -65.3659 -inf yes 100.00%
sm -65.3659 -inf yes 100.00%
smac -44.4311 -inf no 66.67%

Run time 100s

method value bound optimal accuracy
dd-ls0 -65.3659 -65.3772 yes 100.00%
dd-ls3 -65.3659 -65.3846 yes 100.00%
dd-ls4 -65.3659 -65.3663 yes 100.00%
fgmd -65.3659 -inf yes 100.00%
fm-bca -65.3659 -65.3659 yes 100.00%
fm -65.3659 -78.8203 yes 100.00%
fw 0 -inf no 0.00%
ga -65.3659 -inf yes 100.00%
hbp -65.3659 -65.9592 yes 100.00%
ipfps -65.3659 -inf yes 100.00%
ipfpu -65.3659 -inf yes 100.00%
lsm -62.1667 -inf no 93.33%
mp -65.3659 -65.3659 yes 100.00%
mp-fw -65.3659 -65.3659 yes 100.00%
mpm -57.3665 -inf no 83.33%
mp-mcf -65.3659 -65.3659 yes 100.00%
pm -52.6047 -inf no 80.00%
rrwm -65.3659 -inf yes 100.00%
sm -65.3659 -inf yes 100.00%
smac -44.4311 -inf no 66.67%

Run time 300s

method value bound optimal accuracy
dd-ls0 -65.3659 -65.3772 yes 100.00%
dd-ls3 -65.3659 -65.3846 yes 100.00%
dd-ls4 -65.3659 -65.3663 yes 100.00%
fgmd -65.3659 -inf yes 100.00%
fm-bca -65.3659 -65.3659 yes 100.00%
fm -65.3659 -78.8203 yes 100.00%
fw 0 -inf no 0.00%
ga -65.3659 -inf yes 100.00%
hbp -65.3659 -65.9592 yes 100.00%
ipfps -65.3659 -inf yes 100.00%
ipfpu -65.3659 -inf yes 100.00%
lsm -62.1667 -inf no 93.33%
mp -65.3659 -65.3659 yes 100.00%
mp-fw -65.3659 -65.3659 yes 100.00%
mpm -57.3665 -inf no 83.33%
mp-mcf -65.3659 -65.3659 yes 100.00%
pm -52.6047 -inf no 80.00%
rrwm -65.3659 -inf yes 100.00%
sm -65.3659 -inf yes 100.00%
smac -44.4311 -inf no 66.67%

Other Results for this Dataset

Accumulated results for whole dataset: house-sparse

Results for individual instances of the dataset: