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Algorithms and Applications in Computer Vision. Lihi Zelnik -Manor lihi@ee.technion.ac.il ROC. Precision-Recall. circles. Not circles. Truth:. Not circles. Result:. circles. Precision-Recall. circles. Not circles. Truth:. Not circles. FN. Result:. circles. TP. FP. TN.

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Algorithms and Applications in Computer Vision


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    1. Algorithms and Applications in Computer Vision LihiZelnik-Manor lihi@ee.technion.ac.il ROC

    2. Precision-Recall circles Not circles Truth: Not circles Result: circles

    3. Precision-Recall circles Not circles Truth: Not circles FN Result: circles TP FP TN

    4. Confusion matrix P = Positive N = Negative

    5. ROC curve AUC = Area Under Curve

    6. Precision-Recall

    7. F-measure The harmonic mean (mean of rates) of the precision and recall

    8. True/false positives The distance threshold affects performance True positives = # of detected matches that are correct False positives = # of detected matches that are incorrect 50 true match 75 200 false match feature distance

    9. # true positives # matching features (positives) # false positives # unmatched features (negatives) Evaluating the results How can we measure the performance of a feature matcher? 1 0.7 truepositiverate 0 1 false positive rate 0.1

    10. Evaluating the results How can we measure the performance of a feature matcher? # true positives # matching features (positives) # false positives # unmatched features (negatives) ROC curve (“Receiver Operator Characteristic”) 1 0.7 truepositiverate 0 1 false positive rate 0.1 • 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