Artículos de revistas
Nearest Neighbors Distance Ratio Open-set Classifier
Registro en:
Machine Learning. Springer, v. 106, p. 359 - 386, 2017.
0885-6125
1573-0565
WOS:000394355700002
10.1007/s10994-016-5610-8
Autor
Mendes Junior
Pedro R.; de Souza
Roberto M.; Werneck
Rafael de O.; Stein
Bernardo V.; Pazinato
Daniel V.; de Almeida
Waldir R.; Penatti
Otavio A. B.; Torres
Ricardo da S.; Rocha
Anderson
Institución
Resumen
Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) 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. 106 3 359 386 Samsung Eletronica da Amazonia Ltda. [8248/91] Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq) [304352/2012-8, 304472/2015-8] Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES) Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP) [2010/05647-4, 2013/50169-1, 2013/50155-0] DejaVu grant [2015/19222-9] Microsoft Research Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)