Nearest neighbors distance ratio open set classifier
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.
Machine Learning