  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
decision 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 HDP, our CD-OSR does not need to define the decision
threshold and can implement the open set recognition and new class discovery
simultaneously. Finally, extensive experiments on benchmark datasets indicate
the validity of CD-OSR.
