Abstract

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.

摘要

本文提出了一种基于使用极值统计的开放集识别模型。为此，引入了一个距离比，它通过考虑目标点周围局部距离的比率来表示目标点与已知类的不同程度。结果表明，具有有界支持的广义帕累托分布类可用于对高于高阈值的距离比的峰值进行建模。生成的分布提供了一个执行开放集识别的概率框架。此外，我们描述了一个数值程序来估计我们模型的超参数。此过程基于一个新的目标函数，该函数同时考虑广义帕累托分布的拟合和已知类的错误分类误差。我们的方法应用于三个图像数据集和一个音频数据集，表明它优于类似的开放集识别和异常检测方法。本文的补充材料可在线获取。