Statistical Performance Metrics for Use with Imprecise Ground-Truth

Abstract : This paper addresses performance evaluation in the presence of imprecise ground-truth. Indeed, the most common assumption when performing benchmarking measures is that the reference data is awless. In previous work, we have shown that this assumption cannot be taken for granted, and that, in the case of perceptual interpretation problems it is most certainly always wrong but for the most trivial cases. We are presenting a statistical test that will allow measuring the con-dence one can have in the results of a benchmarking test ranking multiple algorithms. More specically, we can express the probability of the ranking not being respected in the presence of a given level of errors in the ground truth data.
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Bart Lamiroy; Rafael Dueire Lins. Graphics Recognition. Current Trends and Challenges: 11th International Workshop on Graphics Recognition, GREC 2015, Aug 2015, Nancy, France. Springer, 9657, 2017, Lecture Notes In Computer Science. 〈10.1007/978-3-319-52159-6_3〉
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Bart Lamiroy, Pascal Pierrot. Statistical Performance Metrics for Use with Imprecise Ground-Truth. Bart Lamiroy; Rafael Dueire Lins. Graphics Recognition. Current Trends and Challenges: 11th International Workshop on Graphics Recognition, GREC 2015, Aug 2015, Nancy, France. Springer, 9657, 2017, Lecture Notes In Computer Science. 〈10.1007/978-3-319-52159-6_3〉. 〈hal-01401034〉

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