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.
Open set recognition for automatic target classification with rejection
IEEE Transactions on Aerospace and Electronic Systems ; 52 , 2 ; 632-642
2016-04-01
764466 byte
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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