Title: Collective decision for open set recognition,
Abstract: In open set recognition (OSR), almost all existing methods are designed
specially for recognizing individual instances, even these instances are
collectively coming in batch. Recognizers in decision either reject or
categorize them to some known class using empirically-set threshold. Thus the
threshold plays a key role, however, the selection for it usually depends on
the knowledge of known classes, inevitably incurring risks due to lacking
available information from unknown classes. On the other hand, a more realistic
OSR system should NOT just rest on a reject decision but should go further,
especially for discovering the hidden unknown classes among the reject
instances, whereas existing OSR methods do not pay special attention. In this
paper, we introduce a novel collective/batch decision strategy with an aim to
extend existing OSR for new class discovery while considering correlations
among the testing instances. Specifically, a collective decision-based OSR
framework (CD-OSR) is proposed by slightly modifying the Hierarchical Dirichlet
process (HDP). Thanks to the HDP, our CD-OSR does not need to define the
specific threshold and can automatically reserve space for unknown classes in
testing, naturally resulting in a new class discovery function. Finally,
extensive experiments on benchmark datasets indicate the validity of CD-OSR.