Computation of Match Metric in matchFeatures

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Jonathan Bittner
Jonathan Bittner 2020-3-17
评论： Jonathan Bittner 2020-10-8
I'm using the matchFeatures function to match two sets of SURF features. Running with the default arguments, I know that sum-of-squares distances (SSD) are computed and both a threshold test and ratio test are used. I'm a little curious at how the threshold test works. Ratio tests and nearest neighbor matching are well documented.
From generating C code output, I know with the default threshold parameter of 1%, match metrics greater than 0.4 are rejected. This is because (per the documentation) ideal matches have a metric of 0 and the worst matches have a metric of 4. Everything else is buried in library code.
If I were trying to replicate this threshold test in Python (or even C++ without Coder) and OpenCV, what kind of math would I be doing to compute the metrics? Why is the max distance 4? Is there some sort of normalization or statistical technique being used?

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Prabhan Purwar
Prabhan Purwar 2020-3-27
Hi,
  • Each feature vector is normalized before computing similarity score between feature vectors. This brings each value of feature vector to range [-1,1].
Normalization =(fVec-mean(fVec))/norm(fVec);
  • SSD is computed using formula
(|| fVec1 - fVec2 ||L2 ) / || fVec2||);
and match score computed between normalized feature vectors rather than exact feature vectors.
  • Ideal matches have a metric of 0 and the worst matches have a metric of 4 as maximum value of the above equation is 4 when fVec1 = -fVec2 and min is 0 when fVec1 = fVec2.
Hope it helps!!
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Jonathan Bittner
Jonathan Bittner 2020-10-8
I'm just seeing this now as I went in to look for an archived answer to one of my other questions. Thank you! I believe this answered my question.

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