Collective decision for open set recognition

TL;DR

This paper introduces a novel collective decision strategy for open set recognition that leverages hierarchical Bayesian modeling to simultaneously recognize known classes and discover new ones without relying on fixed thresholds.

Contribution

It proposes the CD-OSR framework, which extends existing OSR methods by enabling new class discovery and eliminating the need for predefined decision thresholds.

Findings

Effective in recognizing known classes and discovering new classes

Does not require setting a decision threshold

Validated on benchmark datasets

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 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.