A Comparative Study of Graph Matching Algorithms in Computer Vision

Benchmark Results for “house-sparse61”

This page shows the benchmarks results for the dataset instance “house-sparse61”. We consider solutions as optimal if the objective value is within a 0.1% range of the known optimum -66.4981.

Run time 1s

method value bound optimal accuracy
dd-ls0 -66.4981 -66.505 yes 100.00%
dd-ls3 -66.4981 -66.4982 yes 100.00%
dd-ls4 -53.1765 -70.5881 no 83.33%
fgmd inf -inf no
fm-bca -66.4981 -66.4981 yes 100.00%
fm -66.4981 -78.8734 yes 100.00%
fw 0 -inf no 0.00%
ga -66.4981 -inf yes 100.00%
hbp -66.4981 -66.8598 yes 100.00%
ipfps -66.4981 -inf yes 100.00%
ipfpu -66.4981 -inf yes 100.00%
lsm -63.3941 -inf no 93.33%
mp -66.4981 -66.4981 yes 100.00%
mp-fw -66.4981 -66.4981 yes 100.00%
mpm -61.7394 -inf no 90.00%
mp-mcf -66.4981 -66.4981 yes 100.00%
pm -52.07 -inf no 80.00%
rrwm -66.4981 -inf yes 100.00%
sm -59.8398 -inf no 93.33%
smac -66.4981 -inf yes 100.00%

Run time 10s

method value bound optimal accuracy
dd-ls0 -66.4981 -66.505 yes 100.00%
dd-ls3 -66.4981 -66.4982 yes 100.00%
dd-ls4 -66.4981 -66.5026 yes 100.00%
fgmd -66.4981 -inf yes 100.00%
fm-bca -66.4981 -66.4981 yes 100.00%
fm -66.4981 -78.8734 yes 100.00%
fw 0 -inf no 0.00%
ga -66.4981 -inf yes 100.00%
hbp -66.4981 -66.8598 yes 100.00%
ipfps -66.4981 -inf yes 100.00%
ipfpu -66.4981 -inf yes 100.00%
lsm -63.3941 -inf no 93.33%
mp -66.4981 -66.4981 yes 100.00%
mp-fw -66.4981 -66.4981 yes 100.00%
mpm -61.7394 -inf no 90.00%
mp-mcf -66.4981 -66.4981 yes 100.00%
pm -52.07 -inf no 80.00%
rrwm -66.4981 -inf yes 100.00%
sm -59.8398 -inf no 93.33%
smac -66.4981 -inf yes 100.00%

Run time 100s

method value bound optimal accuracy
dd-ls0 -66.4981 -66.505 yes 100.00%
dd-ls3 -66.4981 -66.4982 yes 100.00%
dd-ls4 -66.4981 -66.5026 yes 100.00%
fgmd -66.4981 -inf yes 100.00%
fm-bca -66.4981 -66.4981 yes 100.00%
fm -66.4981 -78.8734 yes 100.00%
fw 0 -inf no 0.00%
ga -66.4981 -inf yes 100.00%
hbp -66.4981 -66.8598 yes 100.00%
ipfps -66.4981 -inf yes 100.00%
ipfpu -66.4981 -inf yes 100.00%
lsm -63.3941 -inf no 93.33%
mp -66.4981 -66.4981 yes 100.00%
mp-fw -66.4981 -66.4981 yes 100.00%
mpm -61.7394 -inf no 90.00%
mp-mcf -66.4981 -66.4981 yes 100.00%
pm -52.07 -inf no 80.00%
rrwm -66.4981 -inf yes 100.00%
sm -59.8398 -inf no 93.33%
smac -66.4981 -inf yes 100.00%

Run time 300s

method value bound optimal accuracy
dd-ls0 -66.4981 -66.505 yes 100.00%
dd-ls3 -66.4981 -66.4982 yes 100.00%
dd-ls4 -66.4981 -66.5026 yes 100.00%
fgmd -66.4981 -inf yes 100.00%
fm-bca -66.4981 -66.4981 yes 100.00%
fm -66.4981 -78.8734 yes 100.00%
fw 0 -inf no 0.00%
ga -66.4981 -inf yes 100.00%
hbp -66.4981 -66.8598 yes 100.00%
ipfps -66.4981 -inf yes 100.00%
ipfpu -66.4981 -inf yes 100.00%
lsm -63.3941 -inf no 93.33%
mp -66.4981 -66.4981 yes 100.00%
mp-fw -66.4981 -66.4981 yes 100.00%
mpm -61.7394 -inf no 90.00%
mp-mcf -66.4981 -66.4981 yes 100.00%
pm -52.07 -inf no 80.00%
rrwm -66.4981 -inf yes 100.00%
sm -59.8398 -inf no 93.33%
smac -66.4981 -inf yes 100.00%

Other Results for this Dataset

Accumulated results for whole dataset: house-sparse

Results for individual instances of the dataset: