Hierarchical Dirichlet Process-based Open Set Recognition
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In this paper, we proposed a novel hierarchical dirichlet process-based classification framework for open set recognition (HDP-OSR) where new categories' samples unseen in training appear during testing. Unlike the existing methods which deal with this problem from the perspective of discriminative model, we reconsider this problem from the perspective of generative model. We model each known class data in training set as a group in hierarchical dirichlet process (HDP) while the testing set as a whole is treated in the same way, then co-clustering all the groups under the HDP framework. Based on the properties of HDP, our HDP-OSR does not overly depend on training samples and can achieve adaptive change as the data changes. More precisely, HDP-OSR can automatically reserve space for unknown categories while it can also discover new categories, meaning it naturally adapts to the open set recognition scenario. Furthermore, treating the testing set as a whole makes our framework take the correlations among the testing samples into account whereas the existing methods obviously ignore this information. Experimental results on a set of benchmark data sets indicate the validity of our learning framework.
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