How to apply Ratio Test in order to remove outliers in a multiple object detection matcher?

How to apply Ratio Test in order to remove outliers in a multiple object
detection matcher?

I'm developing an iPad prototype that needs to detect multiple instances
of an known object. I'm using SIFT for feature detection and descriptor
extraction and BruteForce matcher.
The first problem I had to solve was how to cluster the matches in order
to separate each object instance in the scene. This was done by finding
the nearest matches, using two or tree steps for best results.
Now I'm working in how to reduce the outliers and I'm trying to use the
Ratio Test in order to remove them. So, I have two questions here, someone
could help me with them or maybe with better ways?:
1) Before, I was using the radiusMatch, but when I try to use the knnMatch
(with k > 1), I receive a runtime error. Does BruteForce matcher supports
knnMatcher? I didn't find anything about that.
2) How do I apply the Ratio Test with multiple instances? I mean, if I use
knnMatch, I assume that I'll need to pass the k parameter with the
aproximate number of expected objects instances, so, how can I apply the
test with, for example, k = 20?
Reference from Mastering_OpenCV:
Ratio test The second well-known outlier-removal technique is the ratio
test. We perform KNN-matching first with K=2. Two nearest descriptors are
returned for each match. The match is returned only if the distance ratio
between the first and second matches is big enough (the ratio threshold is
usually near two).
Tks