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 openset recognition regimes and demonstrate that the proposed OSNN significantly outperforms their counterparts in the literature.

The classification accuracy obtained by the proposed method with the novel descriptor in the ultrasound tissue images (around 73%) is significantly above the accuracy of the state-of-the-art threshold-based methods (around 54%). The results are validated by statistical tests. The correlation between the virtual and real histology confirms the quality of the proposed approach showing it is a robust ally for the virtual histology in ultrasound images.

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