Which makes faster scans, increasing or decreasing similarity

I made this test:

x = 50
for s in [0.2, 0.99]:
 start = time.time()
 for i in range(20):
  m = find(Pattern("some_image.png").similar(s))
 print '***', s, (time.time()-start)*1000/x

 m.highlight(2)

In general, it seems to be, that searching with 0.2 is faster than searching with 0.99 (the match always has a similarity of 1.0). The difference increases with increased size of the searched image.

e.g. searching on a screen of 1280x800 in average an image of 600 x 500 is matched at 0.2 after 350 msec and with 0.99 after 750 msec. - with 100 x 150 it is about 130 to 150.

But be aware, lowering the min similarity increases the risk to match the wrong object.

one more thing:
the best way to speed up searches is to make the region of interest as small as possible.

I have to correct the absolute values, since there is a "bug" in the script:

x = 50
for s in [0.2, 0.99]:
 start = time.time()
 for i in range(x):
  m = find(Pattern("some_image.png").similar(s))
 print '***', s, (time.time()-start)*1000/x

 m.highlight(2)

e.g. searching on a screen of 1280x800 in average an image of 600 x 500 is matched at 0.2 after 1.1 sec and with 0.99 after 2.3 sec.