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Flexible Statistical Learning Model for Unsupervised Image Modeling and Segmentation

  • Ines Channoufi
  • Fatma Najar
  • Sami BourouisEmail author
  • Muhammad Azam
  • Alrence S. Halibas
  • Roobaea Alroobaea
  • Ali Al-Badi
Chapter
Part of the Unsupervised and Semi-Supervised Learning book series (UNSESUL)

Abstract

We propose in this work to improve the tasks of image segmentation and modeling through an unsupervised flexible learning approach. Our focus here is to develop an alternative mixture model based on a bounded generalized Gaussian distribution, which is less sensitive to over-segmentation and offers more flexibility in data modeling than the Gaussian distribution which is certainly not the best approximation for image segmentation. A maximum likelihood- (ML) based algorithm is applied for estimating the resulted model parameters. We investigate here the integration of both a spatial information (a prior information between neighboring pixels) and a minimum description length (MDL) principle into the model learning step in order to deal with the major problems of finding the optimal number of classes and also selecting the best model that describes accurately the dataset. Therefore, the proposed model has the advantage to maintain the balance between model complexity and goodness of fit. Obtained results on a large database of medical MR images confirm the effectiveness of the proposed approach and demonstrate its superior performance compared to some conventional methods.

Keywords

Unsupervised learning Generalized Gaussian distribution Maximum likelihood Spatial information Minimum description length Image segmentation 

Notes

Acknowledgements

This research is based on a grant received from the research council (TCR)-Oman.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Ines Channoufi
    • 1
  • Fatma Najar
    • 2
  • Sami Bourouis
    • 3
    • 4
    Email author
  • Muhammad Azam
    • 5
  • Alrence S. Halibas
    • 6
  • Roobaea Alroobaea
    • 7
  • Ali Al-Badi
  1. 1.Université de Tunis El Manar, Ecole Nationale d’Ingénieurs de TunisLR-SITI Laboratoire Signal, Image et Technologies de l’InformationTunisTunisia
  2. 2.Laboratoire RISC Robotique Informatique et Systémes ComplexesUniversité de Tunis El ManarENITTunisia
  3. 3.Taif UniversityTaifSaudi Arabia
  4. 4.Université de Tunis El Manar, LR-SITI Laboratoire SignalImage et Technologies de l’InformationTunisTunisia
  5. 5.Department of Electrical and Computer Engineering (ECE)Concordia UniversityMontrealCanada
  6. 6.Gulf CollegeAl MaabelahMuscatOman
  7. 7.Taif UniversityTaifSaudi Arabia

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