What the progress does is the follwing, based on %SMP
- %SMP stands as you say for percentage of samples
- During the first stage a single feature is used to classify all images, you will get 100% usage of the samples at that moment.
- Secondly, all the samples that were classified wrong by the feature, are treated in the next feature, for again classifying positive and negative samples.
- This continues, so the percentage shows the amount of elements the weak classifier in the previous step did not succeed to classify correctly.
Does this makes any sense?
