IEEE Trans Pattern Anal Mach Intell. 2017 Aug;39(8):1690-1696. doi: 10.1109/TPAMI.2016.2613924. Epub 2016 Sep 27.
We propose a generalized Sparse Representation-based Classification (SRC) algorithm for open set recognition where not all classes presented during testing are known during training. The SRC algorithm uses class reconstruction errors for classification. As most of the discriminative information for open set recognition is hidden in the tail part of the matched and sum of non-matched reconstruction error distributions, we model the tail of those two error distributions using the statistical Extreme Value Theory (EVT). Then we simplify the open set recognition problem into a set of hypothesis testing problems. The confidence scores corresponding to the tail distributions of a novel test sample are then fused to determine its identity. The effectiveness of the proposed method is demonstrated using four publicly available image and object classification datasets and it is shown that this method can perform significantly better than many competitive open set recognition algorithms.
我们提出了一种广义的基于稀疏表示的分类（SRC）算法，用于开放集识别，其中在测试期间呈现的并非所有类在训练期间都是已知的。SRC 算法使用类重构误差进行分类。由于用于开放集识别的大多数判别信息隐藏在匹配和非匹配重构误差分布的尾部，因此我们使用统计极值理论（EVT）对这两个误差分布的尾部进行建模。然后，我们将开放集识别问题简化为一组假设检验问题。然后融合对应于新测试样本的尾部分布的置信得分来确定其身份。使用四个公开可用的图像和对象分类数据集证明了所提出方法的有效性，并且表明该方法可以显著优于许多有竞争力的开放集识别算法。
