One-Class Multiple Instance Learning and Applications to Target Tracking

  • Karthik Sankaranarayanan
  • James W. Davis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7726)


Existing work in the field of Multiple Instance Learning (MIL) have only looked at the standard two-class problem assuming both positive and negative bags are available. In this work, we propose the first analysis of the one-class version of MIL problem where one is only provided input data in the form of positive bags. We also propose an SVM-based formulation to solve this problem setting. To make the approach computationally tractable we further develop a iterative heuristic algorithm using instance priors. We demonstrate the validity of our approach with synthetic data and compare it with the two-class approach. While previous work in target tracking using MIL have made certain run-time assumptions (such as motion) to address the problem, we generalize the approach and demonstrate the applicability of our work to this problem domain. We develop a scene prior modeling technique to obtain foreground-background priors to aid our one-class MIL algorithm and demonstrate its performance on standard tracking sequences.


Multiple Instance Positive Instance Positive Class Multiple Instance Learn Instance Label 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Karthik Sankaranarayanan
    • 1
  • James W. Davis
    • 2
  1. 1.IBM ResearchIndia
  2. 2.Ohio State UniversityUSA

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