A Comparative Study of Graph Matching Algorithms in Computer Vision

Benchmark Results for “house-sparse74”

This page shows the benchmarks results for the dataset instance “house-sparse74”. We consider solutions as optimal if the objective value is within a 0.1% range of the known optimum -67.5753.

Run time 1s

method value bound optimal accuracy
dd-ls0 -67.5753 -67.5883 yes 100.00%
dd-ls3 -67.5753 -67.5784 yes 100.00%
dd-ls4 -54.8071 -71.2932 no 90.00%
fgmd inf -inf no
fm-bca -67.5753 -67.5753 yes 100.00%
fm -67.5753 -78.85 yes 100.00%
fw 0 -inf no 0.00%
ga -67.5753 -inf yes 100.00%
hbp -67.5753 -67.884 yes 100.00%
ipfps -67.5753 -inf yes 100.00%
ipfpu -67.5753 -inf yes 100.00%
lsm -64.3853 -inf no 93.33%
mp -67.5753 -67.5753 yes 100.00%
mp-fw -67.5753 -67.5753 yes 100.00%
mpm -56.482 -inf no 86.67%
mp-mcf -67.5753 -67.5753 yes 100.00%
pm -50.1432 -inf no 83.33%
rrwm -67.5753 -inf yes 100.00%
sm -67.5753 -inf yes 100.00%
smac -49.5112 -inf no 80.00%

Run time 10s

method value bound optimal accuracy
dd-ls0 -67.5753 -67.5883 yes 100.00%
dd-ls3 -67.5753 -67.5784 yes 100.00%
dd-ls4 -67.5753 -67.5761 yes 100.00%
fgmd -67.5753 -inf yes 100.00%
fm-bca -67.5753 -67.5753 yes 100.00%
fm -67.5753 -78.85 yes 100.00%
fw 0 -inf no 0.00%
ga -67.5753 -inf yes 100.00%
hbp -67.5753 -67.884 yes 100.00%
ipfps -67.5753 -inf yes 100.00%
ipfpu -67.5753 -inf yes 100.00%
lsm -64.3853 -inf no 93.33%
mp -67.5753 -67.5753 yes 100.00%
mp-fw -67.5753 -67.5753 yes 100.00%
mpm -56.482 -inf no 86.67%
mp-mcf -67.5753 -67.5753 yes 100.00%
pm -50.1432 -inf no 83.33%
rrwm -67.5753 -inf yes 100.00%
sm -67.5753 -inf yes 100.00%
smac -49.5112 -inf no 80.00%

Run time 100s

method value bound optimal accuracy
dd-ls0 -67.5753 -67.5883 yes 100.00%
dd-ls3 -67.5753 -67.5784 yes 100.00%
dd-ls4 -67.5753 -67.5761 yes 100.00%
fgmd -67.5753 -inf yes 100.00%
fm-bca -67.5753 -67.5753 yes 100.00%
fm -67.5753 -78.85 yes 100.00%
fw 0 -inf no 0.00%
ga -67.5753 -inf yes 100.00%
hbp -67.5753 -67.884 yes 100.00%
ipfps -67.5753 -inf yes 100.00%
ipfpu -67.5753 -inf yes 100.00%
lsm -64.3853 -inf no 93.33%
mp -67.5753 -67.5753 yes 100.00%
mp-fw -67.5753 -67.5753 yes 100.00%
mpm -56.482 -inf no 86.67%
mp-mcf -67.5753 -67.5753 yes 100.00%
pm -50.1432 -inf no 83.33%
rrwm -67.5753 -inf yes 100.00%
sm -67.5753 -inf yes 100.00%
smac -49.5112 -inf no 80.00%

Run time 300s

method value bound optimal accuracy
dd-ls0 -67.5753 -67.5883 yes 100.00%
dd-ls3 -67.5753 -67.5784 yes 100.00%
dd-ls4 -67.5753 -67.5761 yes 100.00%
fgmd -67.5753 -inf yes 100.00%
fm-bca -67.5753 -67.5753 yes 100.00%
fm -67.5753 -78.85 yes 100.00%
fw 0 -inf no 0.00%
ga -67.5753 -inf yes 100.00%
hbp -67.5753 -67.884 yes 100.00%
ipfps -67.5753 -inf yes 100.00%
ipfpu -67.5753 -inf yes 100.00%
lsm -64.3853 -inf no 93.33%
mp -67.5753 -67.5753 yes 100.00%
mp-fw -67.5753 -67.5753 yes 100.00%
mpm -56.482 -inf no 86.67%
mp-mcf -67.5753 -67.5753 yes 100.00%
pm -50.1432 -inf no 83.33%
rrwm -67.5753 -inf yes 100.00%
sm -67.5753 -inf yes 100.00%
smac -49.5112 -inf no 80.00%

Other Results for this Dataset

Accumulated results for whole dataset: house-sparse

Results for individual instances of the dataset: