A purely Bayesian approach for proportional visual data modelling Online publication date: Tue, 04-Sep-2018
by Sami Bourouis; Yacine Laalaoui; Nizar Bouguila
International Journal of Intelligent Engineering Informatics (IJIEI), Vol. 6, No. 5, 2018
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.
Online publication date: Tue, 04-Sep-2018
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Intelligent Engineering Informatics (IJIEI):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email firstname.lastname@example.org