A Comparative Study of Graph Matching Algorithms in Computer Vision

Benchmark Results for “house-sparse98”

This page shows the benchmarks results for the dataset instance “house-sparse98”. We consider solutions as optimal if the objective value is within a 0.1% range of the known optimum -68.5047.

Run time 1s

method value bound optimal accuracy
dd-ls0 -68.5047 -68.5315 yes 100.00%
dd-ls3 -68.5047 -68.507 yes 100.00%
dd-ls4 -68.5047 -72.204 yes 100.00%
fgmd inf -inf no
fm-bca -68.5047 -68.5047 yes 100.00%
fm -68.5047 -78.894 yes 100.00%
fw 0 -inf no 0.00%
ga -68.5047 -inf yes 100.00%
hbp -68.5047 -68.5047 yes 100.00%
ipfps -68.5047 -inf yes 100.00%
ipfpu -68.5047 -inf yes 100.00%
lsm -68.5047 -inf yes 100.00%
mp -68.5047 -68.5047 yes 100.00%
mp-fw -68.5047 -68.5047 yes 100.00%
mpm -60.2111 -inf no 93.33%
mp-mcf -68.5047 -68.5047 yes 100.00%
pm -57.5911 -inf no 86.67%
rrwm -68.5047 -inf yes 100.00%
sm -68.5047 -inf yes 100.00%
smac -51.384 -inf no 83.33%

Run time 10s

method value bound optimal accuracy
dd-ls0 -68.5047 -68.5315 yes 100.00%
dd-ls3 -68.5047 -68.507 yes 100.00%
dd-ls4 -68.5047 -68.5078 yes 100.00%
fgmd -68.5047 -inf yes 100.00%
fm-bca -68.5047 -68.5047 yes 100.00%
fm -68.5047 -78.894 yes 100.00%
fw 0 -inf no 0.00%
ga -68.5047 -inf yes 100.00%
hbp -68.5047 -68.5047 yes 100.00%
ipfps -68.5047 -inf yes 100.00%
ipfpu -68.5047 -inf yes 100.00%
lsm -68.5047 -inf yes 100.00%
mp -68.5047 -68.5047 yes 100.00%
mp-fw -68.5047 -68.5047 yes 100.00%
mpm -60.2111 -inf no 93.33%
mp-mcf -68.5047 -68.5047 yes 100.00%
pm -57.5911 -inf no 86.67%
rrwm -68.5047 -inf yes 100.00%
sm -68.5047 -inf yes 100.00%
smac -51.384 -inf no 83.33%

Run time 100s

method value bound optimal accuracy
dd-ls0 -68.5047 -68.5315 yes 100.00%
dd-ls3 -68.5047 -68.507 yes 100.00%
dd-ls4 -68.5047 -68.5078 yes 100.00%
fgmd -68.5047 -inf yes 100.00%
fm-bca -68.5047 -68.5047 yes 100.00%
fm -68.5047 -78.894 yes 100.00%
fw 0 -inf no 0.00%
ga -68.5047 -inf yes 100.00%
hbp -68.5047 -68.5047 yes 100.00%
ipfps -68.5047 -inf yes 100.00%
ipfpu -68.5047 -inf yes 100.00%
lsm -68.5047 -inf yes 100.00%
mp -68.5047 -68.5047 yes 100.00%
mp-fw -68.5047 -68.5047 yes 100.00%
mpm -60.2111 -inf no 93.33%
mp-mcf -68.5047 -68.5047 yes 100.00%
pm -57.5911 -inf no 86.67%
rrwm -68.5047 -inf yes 100.00%
sm -68.5047 -inf yes 100.00%
smac -51.384 -inf no 83.33%

Run time 300s

method value bound optimal accuracy
dd-ls0 -68.5047 -68.5315 yes 100.00%
dd-ls3 -68.5047 -68.507 yes 100.00%
dd-ls4 -68.5047 -68.5078 yes 100.00%
fgmd -68.5047 -inf yes 100.00%
fm-bca -68.5047 -68.5047 yes 100.00%
fm -68.5047 -78.894 yes 100.00%
fw 0 -inf no 0.00%
ga -68.5047 -inf yes 100.00%
hbp -68.5047 -68.5047 yes 100.00%
ipfps -68.5047 -inf yes 100.00%
ipfpu -68.5047 -inf yes 100.00%
lsm -68.5047 -inf yes 100.00%
mp -68.5047 -68.5047 yes 100.00%
mp-fw -68.5047 -68.5047 yes 100.00%
mpm -60.2111 -inf no 93.33%
mp-mcf -68.5047 -68.5047 yes 100.00%
pm -57.5911 -inf no 86.67%
rrwm -68.5047 -inf yes 100.00%
sm -68.5047 -inf yes 100.00%
smac -51.384 -inf no 83.33%

Other Results for this Dataset

Accumulated results for whole dataset: house-sparse

Results for individual instances of the dataset: