A Novel Parameter Estimation Algorithm for the Multivariate t-Distribution and Its Application to Computer Vision

  • Chad Aeschliman
  • Johnny Park
  • Avinash C. Kak
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6312)


We present a novel algorithm for approximating the parameters of a multivariate t-distribution. At the expense of a slightly decreased accuracy in the estimates, the proposed algorithm is significantly faster and easier to implement compared to the maximum likelihood estimates computed using the expectation-maximization algorithm. The formulation of the proposed algorithm also provides theoretical guidance for solving problems that are intractable with the maximum likelihood equations. In particular, we show how the proposed algorithm can be modified to give an incremental solution for fast online parameter estimation. Finally, we validate the effectiveness of the proposed algorithm by using the approximated t-distribution as a drop in replacement for the conventional Gaussian distribution in two computer vision applications: object recognition and tracking. In both cases the t-distribution gives better performance with no increase in computation.


Approximate Algorithm Multivariate Gaussian Distribution Incremental Algorithm Scale Matrix Parameter Estimation Algorithm 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Chad Aeschliman
    • 1
  • Johnny Park
    • 1
  • Avinash C. Kak
    • 1
  1. 1.Purdue University 

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