A Comparative Study of Graph Matching Algorithms in Computer Vision

Benchmark Results for “house-sparse17”

This page shows the benchmarks results for the dataset instance “house-sparse17”. We consider solutions as optimal if the objective value is within a 0.1% range of the known optimum -66.0531.

Run time 1s

method value bound optimal accuracy
dd-ls0 -66.0531 -66.0539 yes 100.00%
dd-ls3 -66.0531 -66.055 yes 100.00%
dd-ls4 -44.9134 -71.3454 no 73.33%
fgmd inf -inf no
fm-bca -66.0531 -66.0531 yes 100.00%
fm -66.0531 -78.8066 yes 100.00%
fw 0 -inf no 0.00%
ga -66.0531 -inf yes 100.00%
hbp -66.0531 -66.3837 yes 100.00%
ipfps -66.0531 -inf yes 100.00%
ipfpu -66.0531 -inf yes 100.00%
lsm -62.8613 -inf no 93.33%
mp -66.0531 -66.0531 yes 100.00%
mp-fw -66.0531 -66.0531 yes 100.00%
mpm -61.4508 -inf no 90.00%
mp-mcf -66.0531 -66.0531 yes 100.00%
pm -53.3791 -inf no 80.00%
rrwm -66.0531 -inf yes 100.00%
sm -66.0531 -inf yes 100.00%
smac -14.4456 -inf no 0.00%

Run time 10s

method value bound optimal accuracy
dd-ls0 -66.0531 -66.0539 yes 100.00%
dd-ls3 -66.0531 -66.055 yes 100.00%
dd-ls4 -66.0531 -66.0557 yes 100.00%
fgmd -66.0531 -inf yes 100.00%
fm-bca -66.0531 -66.0531 yes 100.00%
fm -66.0531 -78.8066 yes 100.00%
fw 0 -inf no 0.00%
ga -66.0531 -inf yes 100.00%
hbp -66.0531 -66.3837 yes 100.00%
ipfps -66.0531 -inf yes 100.00%
ipfpu -66.0531 -inf yes 100.00%
lsm -62.8613 -inf no 93.33%
mp -66.0531 -66.0531 yes 100.00%
mp-fw -66.0531 -66.0531 yes 100.00%
mpm -61.4508 -inf no 90.00%
mp-mcf -66.0531 -66.0531 yes 100.00%
pm -53.3791 -inf no 80.00%
rrwm -66.0531 -inf yes 100.00%
sm -66.0531 -inf yes 100.00%
smac -14.4456 -inf no 0.00%

Run time 100s

method value bound optimal accuracy
dd-ls0 -66.0531 -66.0539 yes 100.00%
dd-ls3 -66.0531 -66.055 yes 100.00%
dd-ls4 -66.0531 -66.0557 yes 100.00%
fgmd -66.0531 -inf yes 100.00%
fm-bca -66.0531 -66.0531 yes 100.00%
fm -66.0531 -78.8066 yes 100.00%
fw 0 -inf no 0.00%
ga -66.0531 -inf yes 100.00%
hbp -66.0531 -66.3837 yes 100.00%
ipfps -66.0531 -inf yes 100.00%
ipfpu -66.0531 -inf yes 100.00%
lsm -62.8613 -inf no 93.33%
mp -66.0531 -66.0531 yes 100.00%
mp-fw -66.0531 -66.0531 yes 100.00%
mpm -61.4508 -inf no 90.00%
mp-mcf -66.0531 -66.0531 yes 100.00%
pm -53.3791 -inf no 80.00%
rrwm -66.0531 -inf yes 100.00%
sm -66.0531 -inf yes 100.00%
smac -14.4456 -inf no 0.00%

Run time 300s

method value bound optimal accuracy
dd-ls0 -66.0531 -66.0539 yes 100.00%
dd-ls3 -66.0531 -66.055 yes 100.00%
dd-ls4 -66.0531 -66.0557 yes 100.00%
fgmd -66.0531 -inf yes 100.00%
fm-bca -66.0531 -66.0531 yes 100.00%
fm -66.0531 -78.8066 yes 100.00%
fw 0 -inf no 0.00%
ga -66.0531 -inf yes 100.00%
hbp -66.0531 -66.3837 yes 100.00%
ipfps -66.0531 -inf yes 100.00%
ipfpu -66.0531 -inf yes 100.00%
lsm -62.8613 -inf no 93.33%
mp -66.0531 -66.0531 yes 100.00%
mp-fw -66.0531 -66.0531 yes 100.00%
mpm -61.4508 -inf no 90.00%
mp-mcf -66.0531 -66.0531 yes 100.00%
pm -53.3791 -inf no 80.00%
rrwm -66.0531 -inf yes 100.00%
sm -66.0531 -inf yes 100.00%
smac -14.4456 -inf no 0.00%

Other Results for this Dataset

Accumulated results for whole dataset: house-sparse

Results for individual instances of the dataset: