  The performance of a binary classifier is described by a confusion matrix
with four entries: the number of true positives (TP), true negatives (TN),
false positives (FP), and false negatives (FN).
  The Matthew's Correlation Coefficient (MCC), F1, and Fowlkes--Mallows (FM)
scores are scalars that summarize a confusion matrix. Both the F1 and FM scores
are based on only three of the four entries in the confusion matrix (they
ignore TN). In contrast, the MCC takes into account all four entries of the
confusion matrix and thus can be seen as providing a more representative
picture.
  However, in object detection problems, measuring the number of true negatives
is so large it is often intractable. Thus we ask, what happens to the MCC as
the number of true negatives approaches infinity? This paper provides insight
into the relationship between the MCC and FM score by proving that the
FM-measure is equal to the limit of the MCC as the number of true negatives
approaches infinity.
