Binary Feature Descriptors, Performance?
My impression from reading a number of associated papers is that binary feature descriptors would be an order of magnitude faster (or more!) than for instance SIFT.
However, my results in a simple benchmark do not show this. All images + keypoints were pre-cached before running the benchmarks, so it's not that.
DESCRIPTOR MS FEATURES IMAGES MS/FEATURE BRIEF 612 263120 230 0.002326 BRISK 964 263120 230 0.003664 FREAK 564 263120 230 0.002144 ORB 476 247250 230 0.001925 SIFT 1047 247250 230 0.004235
What's the reason for the poor results? Is it because of the implementations in OpenCV? Or am I missing something else?
EDIT: For instance, in the FREAK-paper SIFT-description took 2.5 ms per key point vs 0.018 per key point for FREAK, that is, the latter is about 138 times faster to compute.
Sidenote: AKAZE is missing as a binary descriptor in your evaluation.
is that only the descriptor extraction ? timing the matching might be interseting, too !
It is only descriptor extraction. I will probably do a comparison of matching times within the next few weeks. My main purpose is image recognition though, and not feature tracking.
Guanta, I will read up on AKAZE and perhaps update the test!