A Comparative Study of Graph Matching Algorithms in Computer Vision

Benchmark Results for “house-sparse58”

This page shows the benchmarks results for the dataset instance “house-sparse58”. We consider solutions as optimal if the objective value is within a 0.1% range of the known optimum -66.1722.

Run time 1s

method value bound optimal accuracy
dd-ls0 -66.1722 -66.1772 yes 100.00%
dd-ls3 -66.1722 -66.1731 yes 100.00%
dd-ls4 -66.1722 -71.7669 yes 100.00%
fgmd inf -inf no
fm-bca -66.1722 -66.1722 yes 100.00%
fm -66.1722 -78.8252 yes 100.00%
fw 0 -inf no 0.00%
ga -66.1722 -inf yes 100.00%
hbp -66.1722 -66.2955 yes 100.00%
ipfps -66.1722 -inf yes 100.00%
ipfpu -66.1722 -inf yes 100.00%
lsm -66.1722 -inf yes 100.00%
mp -66.1722 -66.1722 yes 100.00%
mp-fw -66.1722 -66.1722 yes 100.00%
mpm -61.4298 -inf no 90.00%
mp-mcf -66.1722 -66.1722 yes 100.00%
pm -53.2774 -inf no 83.33%
rrwm -66.1722 -inf yes 100.00%
sm -66.1722 -inf yes 100.00%
smac -66.1722 -inf yes 100.00%

Run time 10s

method value bound optimal accuracy
dd-ls0 -66.1722 -66.1772 yes 100.00%
dd-ls3 -66.1722 -66.1731 yes 100.00%
dd-ls4 -66.1722 -66.1743 yes 100.00%
fgmd -66.1722 -inf yes 100.00%
fm-bca -66.1722 -66.1722 yes 100.00%
fm -66.1722 -78.8252 yes 100.00%
fw 0 -inf no 0.00%
ga -66.1722 -inf yes 100.00%
hbp -66.1722 -66.2955 yes 100.00%
ipfps -66.1722 -inf yes 100.00%
ipfpu -66.1722 -inf yes 100.00%
lsm -66.1722 -inf yes 100.00%
mp -66.1722 -66.1722 yes 100.00%
mp-fw -66.1722 -66.1722 yes 100.00%
mpm -61.4298 -inf no 90.00%
mp-mcf -66.1722 -66.1722 yes 100.00%
pm -53.2774 -inf no 83.33%
rrwm -66.1722 -inf yes 100.00%
sm -66.1722 -inf yes 100.00%
smac -66.1722 -inf yes 100.00%

Run time 100s

method value bound optimal accuracy
dd-ls0 -66.1722 -66.1772 yes 100.00%
dd-ls3 -66.1722 -66.1731 yes 100.00%
dd-ls4 -66.1722 -66.1743 yes 100.00%
fgmd -66.1722 -inf yes 100.00%
fm-bca -66.1722 -66.1722 yes 100.00%
fm -66.1722 -78.8252 yes 100.00%
fw 0 -inf no 0.00%
ga -66.1722 -inf yes 100.00%
hbp -66.1722 -66.2955 yes 100.00%
ipfps -66.1722 -inf yes 100.00%
ipfpu -66.1722 -inf yes 100.00%
lsm -66.1722 -inf yes 100.00%
mp -66.1722 -66.1722 yes 100.00%
mp-fw -66.1722 -66.1722 yes 100.00%
mpm -61.4298 -inf no 90.00%
mp-mcf -66.1722 -66.1722 yes 100.00%
pm -53.2774 -inf no 83.33%
rrwm -66.1722 -inf yes 100.00%
sm -66.1722 -inf yes 100.00%
smac -66.1722 -inf yes 100.00%

Run time 300s

method value bound optimal accuracy
dd-ls0 -66.1722 -66.1772 yes 100.00%
dd-ls3 -66.1722 -66.1731 yes 100.00%
dd-ls4 -66.1722 -66.1743 yes 100.00%
fgmd -66.1722 -inf yes 100.00%
fm-bca -66.1722 -66.1722 yes 100.00%
fm -66.1722 -78.8252 yes 100.00%
fw 0 -inf no 0.00%
ga -66.1722 -inf yes 100.00%
hbp -66.1722 -66.2955 yes 100.00%
ipfps -66.1722 -inf yes 100.00%
ipfpu -66.1722 -inf yes 100.00%
lsm -66.1722 -inf yes 100.00%
mp -66.1722 -66.1722 yes 100.00%
mp-fw -66.1722 -66.1722 yes 100.00%
mpm -61.4298 -inf no 90.00%
mp-mcf -66.1722 -66.1722 yes 100.00%
pm -53.2774 -inf no 83.33%
rrwm -66.1722 -inf yes 100.00%
sm -66.1722 -inf yes 100.00%
smac -66.1722 -inf yes 100.00%

Other Results for this Dataset

Accumulated results for whole dataset: house-sparse

Results for individual instances of the dataset: