Non-parametric Bayesian annotator combination

We apply the model to several scenarios related to closed-world classification, open-world classification and novelty detection on a dataset previously published and on two datasets related to plant classification. Our experiments show that NPBAC is able to determine the true number of labels, but also and surprisingly, it largely outperforms the parametric annotator combination by modeling more complex confusions, in particular when few or no training data are available.