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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)

Abstract

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.

Keywords

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

Notes

Acknowledgment

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.

References

  1. 1.
    Allili, M.S., Bouguila, N., Ziou, D.: Finite generalized gaussian mixture modeling and applications to image and video foreground segmentation. In: Proc. of the Fourth Canadian Conference on Computer and Robot Vision (CRV). pp. 183–190 (2007)Google Scholar
  2. 2.
    Amayri, O., Bouguila, N.: On online high-dimensional spherical data clustering and feature selection. Eng. Appl. of AI 26(4), 1386–1398 (2013)Google Scholar
  3. 3.
    Bouguila, N.: A model-based approach for discrete data clustering and feature weighting using MAP and stochastic complexity. IEEE Trans. Knowl. Data Eng. 21(12), 1649–1664 (2009)CrossRefGoogle Scholar
  4. 4.
    Bouguila, N.: Bayesian hybrid generative discriminative learning based on finite liouville mixture models. Pattern Recognition 44(6), 1183–1200 (2011)CrossRefGoogle Scholar
  5. 5.
    Bouguila, N., Ziou, D.: Mml-based approach for finite dirichlet mixture estimation and selection. In: International Workshop on Machine Learning and Data Mining in Pattern Recognition. pp. 42–51. Springer (2005)CrossRefGoogle Scholar
  6. 6.
    Bouguila, N., Ziou, D.: A countably infinite mixture model for clustering and feature selection. Knowl. Inf. Syst. 33(2), 351–370 (2012)CrossRefGoogle Scholar
  7. 7.
    Bourouis, S., Mashrgy, M.A., Bouguila, N.: Bayesian learning of finite generalized inverted dirichlet mixtures: Application to object classification and forgery detection. Expert Systems with Applications 41(5), 2329–2336 (2014)CrossRefGoogle Scholar
  8. 8.
    Channoufi, I., Bourouis, S., Bouguila, N., Hamrouni, K.: Color image segmentation with bounded generalized gaussian mixture model and feature selection. 4th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP’2018) (2018)Google Scholar
  9. 9.
    Channoufi, I., Bourouis, S., Bouguila, N., Hamrouni, K.: Image and video denoising by combining unsupervised bounded generalized gaussian mixture modeling and spatial information. Multimedia Tools and Applications (Feb 2018). https://doi.org/10.1007/s11042-018-5808-9
  10. 10.
    Congdon, P.: Applied Bayesian Modelling. John Wiley and Sons (2003)Google Scholar
  11. 11.
    Elguebaly, Tarek, Bouguila, Nizar: Bayesian Learning of Generalized Gaussian Mixture Models on Biomedical Images. In: Schwenker, Friedhelm, El Gayar, Neamat (eds.) ANNPR 2010. LNCS (LNAI), vol. 5998, pp. 207–218. Springer, Heidelberg (2010).  https://doi.org/10.1007/978-3-642-12159-3_19CrossRefGoogle Scholar
  12. 12.
    Fan, W., Bouguila, N.: Novel approaches for synthesizing video textures. Expert Systems with Applications 39(1), 828–839 (2012)CrossRefGoogle Scholar
  13. 13.
    Fan, W., Bouguila, N.: Variational learning of a dirichlet process of generalized dirichlet distributions for simultaneous clustering and feature selection. Pattern Recognition 46(10), 2754–2769 (2013)CrossRefGoogle Scholar
  14. 14.
    Fan, W., Sallay, H., Bouguila, N., Bourouis, S.: A hierarchical dirichlet process mixture of generalized dirichlet distributions for feature selection. Computers & Electrical Engineering 43, 48–65 (2015)CrossRefGoogle Scholar
  15. 15.
    Fitzgibbon, A., Wexler, Y., Zisserman, A.: Image-based rendering using image-based priors. International Journal of Computer Vision 63(2), 141–151 (2005)CrossRefGoogle Scholar
  16. 16.
    Li, W., Li, B.: Probabilistic image-based rendering with gaussian mixture model. In: 18th International Conference on Pattern Recognition (ICPR’06). vol. 1, pp. 179–182 (2006)Google Scholar
  17. 17.
    Marin, J., Mengersen, K., Robert, C.: Bayesian modeling and inference on mixtures of distributions. In: Dey, D., Rao, C. (eds.) Handbook of Statistics 25. Elsevier-Sciences (2004)Google Scholar
  18. 18.
    McLachlan, G., Peel, D.: Finite mixture models. John Wiley & Sons (2004)Google Scholar
  19. 19.
    Mustafa, A., Kim, H., Guillemaut, J.Y., Hilton, A.: General dynamic scene reconstruction from multiple view video. In: 2015 IEEE International Conference on Computer Vision (ICCV). pp. 900–908 (Dec 2015)Google Scholar
  20. 20.
    Najar, F., Bourouis, S., Bouguila, N., Belguith, S.: A comparison between different gaussian-based mixture models. In: 14th IEEE International Conference on. Computer Systems and Applications, Tunisia. IEEE (2017)Google Scholar
  21. 21.
    Oboh, B.S., Bouguila, N.: Unsupervised learning of finite mixtures using scaled dirichlet distribution and its application to software modules categorization. In: 2017 IEEE International Conference on Industrial Technology (ICIT). pp. 1085–1090 (March 2017)Google Scholar
  22. 22.
    Schödl, A., Essa, I.A.: Machine learning for video-based rendering. In: Advances in neural information processing systems. pp. 1002–1008 (2001)Google Scholar
  23. 23.
    Snavely, N., Simon, I., Goesele, M., Szeliski, R., Seitz, S.M.: Scene reconstruction and visualization from community photo collections. Proceedings of the IEEE 98(8), 1370–1390 (2010)CrossRefGoogle Scholar

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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