A Comparative Study of Graph Matching Algorithms in Computer Vision

Benchmark Results for “caltech-small5”

This page shows the benchmarks results for the dataset instance “caltech-small5”. We consider solutions as optimal if the objective value is within a 0.1% range of the known optimum -1269.04.

Run time 1s

method value bound optimal accuracy
dd-ls0 -1269.04 -1269.09 yes 16.67%
dd-ls3 -1269.04 -1269.08 yes 16.67%
dd-ls4 -1269.04 -1269.04 yes 16.67%
fgmd inf -inf no
fm-bca -1269.04 -1428.12 yes 16.67%
fm -1269.04 -2959.63 yes 16.67%
fw 0 -inf no 0.00%
ga inf -inf no
hbp inf -inf no
ipfps -1144.23 -inf no 66.67%
ipfpu -1142.29 -inf no 66.67%
lsm 0 -inf no 0.00%
mp -1114.05 -1442.35 no 66.67%
mp-fw -1269.04 -1291.81 yes 16.67%
mpm -1004.28 -inf no 66.67%
mp-mcf -1185.12 -1286.01 no 66.67%
pm -727.647 -inf no 33.33%
rrwm -1139.02 -inf no 66.67%
sm -665.284 -inf no 33.33%
smac -1052.46 -inf no 16.67%

Run time 10s

method value bound optimal accuracy
dd-ls0 -1269.04 -1269.09 yes 16.67%
dd-ls3 -1269.04 -1269.08 yes 16.67%
dd-ls4 -1269.04 -1269.04 yes 16.67%
fgmd inf -inf no
fm-bca -1269.04 -1428.12 yes 16.67%
fm -1269.04 -2959.63 yes 16.67%
fw 0 -inf no 0.00%
ga -1191.85 -inf no 66.67%
hbp inf -inf no
ipfps -1144.23 -inf no 66.67%
ipfpu -1142.29 -inf no 66.67%
lsm 0 -inf no 0.00%
mp -1114.05 -1442.35 no 66.67%
mp-fw -1269.04 -1273.36 yes 16.67%
mpm -1004.28 -inf no 66.67%
mp-mcf -1185.12 -1277.49 no 66.67%
pm -727.647 -inf no 33.33%
rrwm -1139.02 -inf no 66.67%
sm -665.284 -inf no 33.33%
smac -1052.46 -inf no 16.67%

Run time 100s

method value bound optimal accuracy
dd-ls0 -1269.04 -1269.09 yes 16.67%
dd-ls3 -1269.04 -1269.08 yes 16.67%
dd-ls4 -1269.04 -1269.04 yes 16.67%
fgmd -1269.04 -inf yes 16.67%
fm-bca -1269.04 -1428.12 yes 16.67%
fm -1269.04 -2959.63 yes 16.67%
fw 0 -inf no 0.00%
ga -1191.85 -inf no 66.67%
hbp -1098.9 -5830.94 no 66.67%
ipfps -1144.23 -inf no 66.67%
ipfpu -1142.29 -inf no 66.67%
lsm 0 -inf no 0.00%
mp -1114.05 -1442.35 no 66.67%
mp-fw -1269.04 -1269.04 yes 16.67%
mpm -1004.28 -inf no 66.67%
mp-mcf -1269.04 -1269.04 yes 16.67%
pm -727.647 -inf no 33.33%
rrwm -1139.02 -inf no 66.67%
sm -665.284 -inf no 33.33%
smac -1052.46 -inf no 16.67%

Run time 300s

method value bound optimal accuracy
dd-ls0 -1269.04 -1269.09 yes 16.67%
dd-ls3 -1269.04 -1269.08 yes 16.67%
dd-ls4 -1269.04 -1269.04 yes 16.67%
fgmd -1269.04 -inf yes 16.67%
fm-bca -1269.04 -1428.12 yes 16.67%
fm -1269.04 -2959.63 yes 16.67%
fw 0 -inf no 0.00%
ga -1191.85 -inf no 66.67%
hbp -1098.9 -5830.94 no 66.67%
ipfps -1144.23 -inf no 66.67%
ipfpu -1142.29 -inf no 66.67%
lsm 0 -inf no 0.00%
mp -1114.05 -1442.35 no 66.67%
mp-fw -1269.04 -1269.04 yes 16.67%
mpm -1004.28 -inf no 66.67%
mp-mcf -1269.04 -1269.04 yes 16.67%
pm -727.647 -inf no 33.33%
rrwm -1139.02 -inf no 66.67%
sm -665.284 -inf no 33.33%
smac -1052.46 -inf no 16.67%

Other Results for this Dataset

Accumulated results for whole dataset: caltech-small

Results for individual instances of the dataset: