article

A purely Bayesian approach for proportional visual data modelling

International Journal of Intelligent Engineering Informatics, Volume 6, Issue 5

Pages 491 - 508

https://doi.org/10.1504/IJIEI.2018.094513

Published: 01 January 2018 Publication History

Abstract

In this paper, we focus on constructing new flexible and powerful parametric framework for proportional visual data modelling. In particular, we propose a Bayesian density estimation method based upon mixtures of scaled Dirichlet distributions. The consideration of Bayesian learning is interesting in several aspects. It allows simultaneous parameters estimation and model selection, it permits also taking uncertainty into account by introducing prior information about the parameters and it allows overcoming learning problems related to over-or under-fitting. In this work, three key issues related to the Bayesian mixture learning are addressed which are the choice of prior distributions, the estimation of the parameters, and the selection of the number of components. Moreover, a principled Metropolis-within-Gibbs sampler algorithm for scaled Dirichlet mixtures is developed. Finally, the proposed Bayesian framework is tested via two challenging real-life applications namely scene reconstruction and face age estimation from images. The obtained results show the merits of our approach.

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Cited By

Galli B(2020)Effective and Ineffective Statistical Analysis Tools in Project Management EnvironmentsInternational Journal of Applied Logistics10.4018/IJAL.202001010410:1(41-57)Online publication date: 1-Jan-2020
https://dl.acm.org/doi/10.4018/IJAL.2020010104
Bourouis SLaalaoui YBouguila N(2019)Bayesian frameworks for traffic scenes monitoring via view-based 3D cars models recognitionMultimedia Tools and Applications10.1007/s11042-019-7275-378:13(18813-18833)Online publication date: 1-Jul-2019
https://dl.acm.org/doi/10.1007/s11042-019-7275-3

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cover image International Journal of Intelligent Engineering Informatics

International Journal of Intelligent Engineering Informatics Volume 6, Issue 5

January 2018

92 pages

ISSN:1758-8715

EISSN:1758-8723

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Inderscience Publishers

Geneva 15, Switzerland

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Published: 01 January 2018

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Cited By

Galli B(2020)Effective and Ineffective Statistical Analysis Tools in Project Management EnvironmentsInternational Journal of Applied Logistics10.4018/IJAL.202001010410:1(41-57)Online publication date: 1-Jan-2020
https://dl.acm.org/doi/10.4018/IJAL.2020010104
Bourouis SLaalaoui YBouguila N(2019)Bayesian frameworks for traffic scenes monitoring via view-based 3D cars models recognitionMultimedia Tools and Applications10.1007/s11042-019-7275-378:13(18813-18833)Online publication date: 1-Jul-2019
https://dl.acm.org/doi/10.1007/s11042-019-7275-3

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