Visual Scene Reconstruction Using a Bayesian Learning Framework

  • Sami BourouisEmail author
  • Nizar Bouguila
  • Yexing Li
  • Muhammad Azam
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10884)


In this paper, we focus on constructing new flexible and powerful parametric framework for visual data modeling and reconstruction. 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 respects. 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 on a challenging real-life application namely visual scene reconstruction.


Mixture of scaled Dirichlet distribution Bayesian inference Markov chain Monte Carlo algorithm Scene reconstruction 



The first author would like to thank the Deanship of Scientific Research at Taif University for the continuous support. This work was supported under the grant number 1-437-5047.


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Sami Bourouis
    • 1
    • 2
    Email author
  • Nizar Bouguila
    • 3
  • Yexing Li
    • 3
  • Muhammad Azam
    • 3
  1. 1.Department of Information TechnologyCollege of Computers and Information Technology, Taif UniversityTaifSaudi Arabia
  2. 2.Université de Tunis El Manar, ENIT, LR-SITI LabTunisTunisia
  3. 3.The Concordia Institute for Information Systems Engineering (CIISE) Concordia UniversityMontrealCanada

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