Measure to separate positive & negative examples using SIFT keypoint object detection?

Question

I used SIFT keypoint descriptors for detecting objects in an image. For that, I used best matches and calculated homography matrix.

Using this homography matrix, I found where the object lies in test image.

Now, for samples where object could not be found which has to be checked manually, what could be the measure which can help to distinguish between negative and positive samples.

Presently, using determinant of homography matrix we are separating the samples. Is there a better measure ?

Answer 1

You may use the number of point correspondences(filtered) as a measure which can help to distinguish between negative and positive samples.
Because positive samples always have much more point correspondences than negative samples.