How to choose descriptor thresholds for NNmAP and matching score across the methods evaluated in your bench ? about tf-lift

[kmyi]

mAP is computed by considering all thresholds. Please see other descriptor papers, and study precision recall curves and how area under curve is used.

Thanks,
Kwang

[abenbihi]

Hello thank you for your answer.

I already studied the metric definition and computation. The only thing I am lacking are the thresholds hard values. For NNmAP you suggest using all the thresholds. But if I have a set of matching features with distance from 0.2 to 1.6, i still need to set a step and evaluate NNmAP for thresholds in linspace(0.2, 1.6, step) no ?

Also for the matching score, I have to choose a unique descriptor distance threshold, how can choose it ?

I found some code with hard-coded thresholds values such as the matlab code from oxford vgg affine however these values are not necessarily relevant for other methods such as orb.

Thanks in advance

[kmyi]

I already studied the metric definition and computation. The only thing I am lacking are the thresholds hard values. For NNmAP you suggest using all the thresholds. But if I have a set of matching features with distance from 0.2 to 1.6, i still need to set a step and evaluate NNmAP for thresholds in linspace(0.2, 1.6, step) no ?

You can simply go through the matches in the order of distances, which effectively does infinite intervals.

Also for the matching score, I have to choose a unique descriptor distance threshold, how can choose it ?

We use nearest neighbour matches, i.e. descriptor distance threshold is infinite.

[abenbihi]

Thank you for these recommendations.