Open Set Recognition for Automatic Target Classification With Rejection

被引：64
|
作者：
Scherreik, Matthew D. [1 ]
Rigling, Brian D. [1 ]
机构：
[1] Wright State Univ, Elect Engn, 3640 Colonel Glenn Highway, Dayton, OH 45435 USA
来源：
IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS | 2016年 / 52卷 / 02期
基金：
美国国家科学基金会;
关键词：
SUPPORT;
D O I：
10.1109/TAES.2015.150027
中图分类号：
V [航空、航天];
学科分类号：
08 ; 0825 ;
摘要：
Training sets for supervised classification tasks are usually limited in scope and only contain examples of a few classes. In practice, classes that were not seen in training are given labels that are always incorrect. Open set recognition (OSR) algorithms address this issue by providing classifiers with a rejection option for unknown samples. In this work, we introduce a new OSR algorithm and compare its performance to other current approaches for open set image classification.
引用
收藏
页码：632 / 642
页数：11
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