Open set recognition for automatic target classification with rejection

Training sets for supervised classification tasks are usually limited in scope and only contain examples of a few classes. In practice, classes that were not seen in training are given labels that are always incorrect. Open set recognition (OSR) algorithms address this issue by providing classifiers with a rejection option for unknown samples. In this work, we introduce a new OSR algorithm and compare its performance to other current approaches for open set image classification.