A Comparative Study of Graph Matching Algorithms in Computer Vision

Benchmark Results for “house-sparse15”

This page shows the benchmarks results for the dataset instance “house-sparse15”. We consider solutions as optimal if the objective value is within a 0.1% range of the known optimum -65.1281.

Run time 1s

method value bound optimal accuracy
dd-ls0 -65.1281 -65.1351 yes 100.00%
dd-ls3 -65.1281 -65.129 yes 100.00%
dd-ls4 -48.7524 -71.56 no 76.67%
fgmd inf -inf no
fm-bca -65.1281 -65.1281 yes 100.00%
fm -65.1281 -78.8049 yes 100.00%
fw 0 -inf no 0.00%
ga -65.1281 -inf yes 100.00%
hbp -65.1281 -65.4946 yes 100.00%
ipfps -65.1281 -inf yes 100.00%
ipfpu -65.1281 -inf yes 100.00%
lsm -61.9306 -inf no 93.33%
mp -65.1281 -65.1281 yes 100.00%
mp-fw -65.1281 -65.1281 yes 100.00%
mpm -60.4988 -inf no 90.00%
mp-mcf -65.1281 -65.1281 yes 100.00%
pm -52.4143 -inf no 80.00%
rrwm -65.1281 -inf yes 100.00%
sm -65.1281 -inf yes 100.00%
smac -11.1081 -inf no 0.00%

Run time 10s

method value bound optimal accuracy
dd-ls0 -65.1281 -65.1351 yes 100.00%
dd-ls3 -65.1281 -65.129 yes 100.00%
dd-ls4 -65.1281 -65.1513 yes 100.00%
fgmd -65.1281 -inf yes 100.00%
fm-bca -65.1281 -65.1281 yes 100.00%
fm -65.1281 -78.8049 yes 100.00%
fw 0 -inf no 0.00%
ga -65.1281 -inf yes 100.00%
hbp -65.1281 -65.4946 yes 100.00%
ipfps -65.1281 -inf yes 100.00%
ipfpu -65.1281 -inf yes 100.00%
lsm -61.9306 -inf no 93.33%
mp -65.1281 -65.1281 yes 100.00%
mp-fw -65.1281 -65.1281 yes 100.00%
mpm -60.4988 -inf no 90.00%
mp-mcf -65.1281 -65.1281 yes 100.00%
pm -52.4143 -inf no 80.00%
rrwm -65.1281 -inf yes 100.00%
sm -65.1281 -inf yes 100.00%
smac -11.1081 -inf no 0.00%

Run time 100s

method value bound optimal accuracy
dd-ls0 -65.1281 -65.1351 yes 100.00%
dd-ls3 -65.1281 -65.129 yes 100.00%
dd-ls4 -65.1281 -65.1513 yes 100.00%
fgmd -65.1281 -inf yes 100.00%
fm-bca -65.1281 -65.1281 yes 100.00%
fm -65.1281 -78.8049 yes 100.00%
fw 0 -inf no 0.00%
ga -65.1281 -inf yes 100.00%
hbp -65.1281 -65.4946 yes 100.00%
ipfps -65.1281 -inf yes 100.00%
ipfpu -65.1281 -inf yes 100.00%
lsm -61.9306 -inf no 93.33%
mp -65.1281 -65.1281 yes 100.00%
mp-fw -65.1281 -65.1281 yes 100.00%
mpm -60.4988 -inf no 90.00%
mp-mcf -65.1281 -65.1281 yes 100.00%
pm -52.4143 -inf no 80.00%
rrwm -65.1281 -inf yes 100.00%
sm -65.1281 -inf yes 100.00%
smac -11.1081 -inf no 0.00%

Run time 300s

method value bound optimal accuracy
dd-ls0 -65.1281 -65.1351 yes 100.00%
dd-ls3 -65.1281 -65.129 yes 100.00%
dd-ls4 -65.1281 -65.1513 yes 100.00%
fgmd -65.1281 -inf yes 100.00%
fm-bca -65.1281 -65.1281 yes 100.00%
fm -65.1281 -78.8049 yes 100.00%
fw 0 -inf no 0.00%
ga -65.1281 -inf yes 100.00%
hbp -65.1281 -65.4946 yes 100.00%
ipfps -65.1281 -inf yes 100.00%
ipfpu -65.1281 -inf yes 100.00%
lsm -61.9306 -inf no 93.33%
mp -65.1281 -65.1281 yes 100.00%
mp-fw -65.1281 -65.1281 yes 100.00%
mpm -60.4988 -inf no 90.00%
mp-mcf -65.1281 -65.1281 yes 100.00%
pm -52.4143 -inf no 80.00%
rrwm -65.1281 -inf yes 100.00%
sm -65.1281 -inf yes 100.00%
smac -11.1081 -inf no 0.00%

Other Results for this Dataset

Accumulated results for whole dataset: house-sparse

Results for individual instances of the dataset: