Say for instance, face detection algorithms - I see researchers testing their algorithms and table their results under
FP. What are these?
These terms are validation metrics used for verifying quality of a segmented image. In a scenario where you want to compare a segmented image with ground truth, then taking the ground truth image as base of comparison you can make assumption of taking foreground as "white" pixels and background as "black" pixels in ground-truth. The terms that you referred then would mean:
- True positive (TP) : pixels correctly segmented as foreground
- False positive (FP) : pixels falsely segmented as foreground
- True negative (TN) : pixels correctly detected as background
- False negative (FN) : pixels falsely detected as background
These metrics are then used to calculate sensitivity, specificity and accuracy as:
The sensitivity tells us how likely the test is come back positive in someone who has the characteristic. This is calculated as
The specificity tells us how likely the test is to come back negative in someone who does not have the characteristic. This is calculated as
Hope this helps !!