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Multi-class Open Set Recognition Using Probability of Inclusion

  • Lalit P. Jain
  • Walter J. Scheirer
  • Terrance E. Boult
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8691)

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.

Keywords

Support Vector Machine Positive Class Support Vector Data Description Extreme Value Theory Class Inclusion 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Supplementary material

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Lalit P. Jain
    • 1
  • Walter J. Scheirer
    • 1
    • 2
  • Terrance E. Boult
    • 1
    • 3
  1. 1.University of ColoradomColorado SpringsUSA
  2. 2.Harvard UniversityUSA
  3. 3.Securics, Inc.USA

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