A Comparative Study of Graph Matching Algorithms in Computer Vision

Benchmark Results for “house-sparse100”

This page shows the benchmarks results for the dataset instance “house-sparse100”. We consider solutions as optimal if the objective value is within a 0.1% range of the known optimum -67.678.

Run time 1s

method value bound optimal accuracy
dd-ls0 -67.678 -67.6874 yes 100.00%
dd-ls3 -67.678 -67.6839 yes 100.00%
dd-ls4 -52.7656 -72.7802 no 80.00%
fgmd inf -inf no
fm-bca -67.678 -67.678 yes 100.00%
fm -67.678 -78.8481 yes 100.00%
fw 0 -inf no 0.00%
ga -67.678 -inf yes 100.00%
hbp -67.678 -67.9847 yes 100.00%
ipfps -67.678 -inf yes 100.00%
ipfpu -67.678 -inf yes 100.00%
lsm -64.4886 -inf no 93.33%
mp -67.678 -67.678 yes 100.00%
mp-fw -67.678 -67.678 yes 100.00%
mpm -62.6098 -inf no 90.00%
mp-mcf -67.678 -67.678 yes 100.00%
pm -54.7473 -inf no 80.00%
rrwm -67.678 -inf yes 100.00%
sm -67.678 -inf yes 100.00%
smac -42.3342 -inf no 66.67%

Run time 10s

method value bound optimal accuracy
dd-ls0 -67.678 -67.6874 yes 100.00%
dd-ls3 -67.678 -67.6839 yes 100.00%
dd-ls4 -67.678 -67.6791 yes 100.00%
fgmd -67.678 -inf yes 100.00%
fm-bca -67.678 -67.678 yes 100.00%
fm -67.678 -78.8481 yes 100.00%
fw 0 -inf no 0.00%
ga -67.678 -inf yes 100.00%
hbp -67.678 -67.9847 yes 100.00%
ipfps -67.678 -inf yes 100.00%
ipfpu -67.678 -inf yes 100.00%
lsm -64.4886 -inf no 93.33%
mp -67.678 -67.678 yes 100.00%
mp-fw -67.678 -67.678 yes 100.00%
mpm -62.6098 -inf no 90.00%
mp-mcf -67.678 -67.678 yes 100.00%
pm -54.7473 -inf no 80.00%
rrwm -67.678 -inf yes 100.00%
sm -67.678 -inf yes 100.00%
smac -42.3342 -inf no 66.67%

Run time 100s

method value bound optimal accuracy
dd-ls0 -67.678 -67.6874 yes 100.00%
dd-ls3 -67.678 -67.6839 yes 100.00%
dd-ls4 -67.678 -67.6791 yes 100.00%
fgmd -67.678 -inf yes 100.00%
fm-bca -67.678 -67.678 yes 100.00%
fm -67.678 -78.8481 yes 100.00%
fw 0 -inf no 0.00%
ga -67.678 -inf yes 100.00%
hbp -67.678 -67.9847 yes 100.00%
ipfps -67.678 -inf yes 100.00%
ipfpu -67.678 -inf yes 100.00%
lsm -64.4886 -inf no 93.33%
mp -67.678 -67.678 yes 100.00%
mp-fw -67.678 -67.678 yes 100.00%
mpm -62.6098 -inf no 90.00%
mp-mcf -67.678 -67.678 yes 100.00%
pm -54.7473 -inf no 80.00%
rrwm -67.678 -inf yes 100.00%
sm -67.678 -inf yes 100.00%
smac -42.3342 -inf no 66.67%

Run time 300s

method value bound optimal accuracy
dd-ls0 -67.678 -67.6874 yes 100.00%
dd-ls3 -67.678 -67.6839 yes 100.00%
dd-ls4 -67.678 -67.6791 yes 100.00%
fgmd -67.678 -inf yes 100.00%
fm-bca -67.678 -67.678 yes 100.00%
fm -67.678 -78.8481 yes 100.00%
fw 0 -inf no 0.00%
ga -67.678 -inf yes 100.00%
hbp -67.678 -67.9847 yes 100.00%
ipfps -67.678 -inf yes 100.00%
ipfpu -67.678 -inf yes 100.00%
lsm -64.4886 -inf no 93.33%
mp -67.678 -67.678 yes 100.00%
mp-fw -67.678 -67.678 yes 100.00%
mpm -62.6098 -inf no 90.00%
mp-mcf -67.678 -67.678 yes 100.00%
pm -54.7473 -inf no 80.00%
rrwm -67.678 -inf yes 100.00%
sm -67.678 -inf yes 100.00%
smac -42.3342 -inf no 66.67%

Other Results for this Dataset

Accumulated results for whole dataset: house-sparse

Results for individual instances of the dataset: