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An Accurate Evaluation of MSD Log-likelihood and its Application in Human Action Recognition

Citation Author(s):
Nuha Zamzami, and Nizar Bouguila
Submitted by:
Nuha Zamzami
Last updated:
9 November 2019 - 7:08am
Document Type:
Presentation Slides
Document Year:
2019
Event:
Presenters Name:
Nizar Bouguila
Paper Code:
1570561537

Abstract 

Abstract: 

In this paper, we examine the problem of modeling overdispersed frequency vectors that are naturally generated by several machine learning and computer vision applications.
We consider a statistical framework based on a mixture of Multinomial Scaled Dirichlet (MSD) distributions that we have previously proposed in [1]. Given that the likelihood function plays a key role in statistical inference, e.g. in maximum likelihood estimation and Fisher information matrix investigation, we propose to improve the efficiency of computing the MSD log-likelihood by approximating its function based on Bernoulli polynomials. As compared to [1], the log-likelihood function is computed using the proposed mesh algorithm and a model selection approach is seamlessly integrated with the parameters estimation. The improved clustering framework offers a good compromise between other techniques and improves the approach used before for the same model. The merits of the proposed approach are validated via a challenging application that involves human action recognition.

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Dataset Files

MSD_MeshPres.pdf

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