Report

Algorithms and Applications in Computer Vision Lihi Zelnik-Manor [email protected] ROC Precision-Recall circles Truth: Not circles Result: circles Not circles Precision-Recall circles Not circles Truth: Not circles FN Result: circles TP FP TN Confusion matrix P = Positive N = Negative Ground-truth E1 E2 E1 obtained result E2 tp (true positive) fn (false negative) fp (false positive) tn (true negative) ROC curve TP TPR P AUC = Area Under Curve FP FPR N Precision-Recall TP Re call TP FN TP Pr ecision TP FP F-measure The harmonic mean (mean of rates) of the precision and recall precision recall F precision recall True/false positives 50 75 true match 200 false match feature distance The distance threshold affects performance • True positives = # of detected matches that are correct • False positives = # of detected matches that are incorrect Evaluating the results How can we measure the performance of a feature matcher? 1 0.7 true # true positives # matching features (positives) positive rate 0 0.1 false positive rate # false positives # unmatched features (negatives) 1 Evaluating the results How can we measure the performance of a feature matcher? ROC curve (“Receiver Operator Characteristic”) 1 0.7 true # true positives # matching features (positives) positive rate 0 0.1 false positive rate 1 # false positives # unmatched features (negatives) ROC Curves • • • • Generated by counting # correct/incorrect matches, for different threholds Want to maximize area under the curve (AUC) Useful for comparing different feature matching methods For more info: http://en.wikipedia.org/wiki/Receiver_operating_characteristic