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Type: Artigo
Title: Nearest Neighbors Distance Ratio Open-set Classifier
Author: 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
Abstract: 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.
Subject: Open-set Recognition
Nearest Neighbor Classifier
Open-set Nearest-neighbor Classifier
Nearest Neighbors Distance Ratio
Open-set Evaluation Measures
Editor: Springer
Rights: fechado
Identifier DOI: 10.1007/s10994-016-5610-8
Date Issue: 2017
Appears in Collections:Unicamp - Artigos e Outros Documentos

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