A Nearest Neighbor Open-Set Classifier based on Excesses of Distance Ratios
提出一种基于极值统计的开集识别模型，通过距离比度量目标点与已知类的差异，用广义帕累托分布建模距离比峰值，在图像和音频数据集上优于同类方法。
This article proposes an open-set recognition model that is based on the use of extreme value statistics. For this purpose, a distance ratio is introduced that expresses how dissimilar a target point is from the known classes by considering the ratio of distances locally around the target point. It is shown that the class of generalized Pareto distributions with bounded support can be used to model the peaks of the distance ratio above a high threshold. The resulting distribution provides a probabilistic framework to perform open-set recognition. Furthermore, we describe a numerical procedure to estimate the hyperparameters of our model. This procedure is based on a new objective function that considers both the fit of the generalized Pareto distribution and the misclassification error of the known classes. Our method is applied to three image datasets and an audio dataset showing that it outperforms similar open-set recognition and anomaly detection methods. Supplementary materials for this article are available online.