In this paper, we propose a novel multiclass classifier for the open-set recognition scenario. This scenario is the one in which there are no a priori training samples for some classes that might appear during testing. Usually, many applications are inherently open set. Consequently, successful closed-set solutions in the literature are not always suitable for real-world recognition problems. The proposed open-set classifier extends upon the Nearest-Neighbor (NN) classifier. Nearest neighbors are simple, parameter independent, multiclass, and widely used for closed-set problems. The proposed Open-Set NN (OSNN) method incorporates the ability of recognizing samples belonging to classes that are unknown at training time, being suitable for open-set recognition. In addition, we explore evaluation measures for open-set problems, properly measuring the resilience of methods to unknown classes during testing. For validation, we consider large freely-available benchmarks with different open-set recognition regimes and demonstrate that the proposed OSNN significantly outperforms their counterparts in the literature.
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Mendes Júnior, P.R., de Souza, R.M., Werneck, R.d.O. et al. Nearest neighbors distance ratio open-set classifier. Mach Learn 106, 359–386 (2017). https://doi.org/10.1007/s10994-016-5610-8
- Open-set recognition
- Nearest neighbor classifier
- Open-set nearest-neighbor classifier
- Nearest neighbors distance ratio
- Open-set evaluation measures