Multi-class Open Set Recognition Using Probability of Inclusion^*

Lalit P. Jain¹, Walter J. Scheirer^1,2, and Terrance E. Boult^1,3

¹University of Coloradom, Colorado Springs, USA

²Harvard University, USA

³Securics, Inc., USA

Abstract. The perceived success of recent visual recognition approaches has largely been derived from their performance on classification tasks, where all possible classes are known at training time. But what about open set problems, where unknown classes appear at test time? Intuitively, if we could accurately model just the positive data for any known class without overfitting, we could reject the large set of unknown classes even under an assumption of incomplete class knowledge. In this paper, we formulate the problem as one of modeling positive training data at the decision boundary, where we can invoke the statistical extreme value theory. A new algorithm called the P_I-SVM is introduced for estimating the unnormalized posterior probability of class inclusion.

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LNCS 8691, p. 393 ff.

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