Computation of Match Metric in matchFeatures

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?

Answer 1

[Prabhan Purwar]

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!!

[Jonathan Bittner]

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.