Spatially Constrained Mixture Model with Feature Selection for Image and Video Segmentation

  • Ines ChannoufiEmail author
  • Sami Bourouis
  • Nizar Bouguila
  • Kamel Hamrouni
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10884)


In this paper we propose to improve image and video sequences segmentation through the integration of feature selection process into an unsupervised learning approach based on a finite mixture of bounded generalized Gaussian distributions (BGGMD). The proposed algorithm is less sensitive to over-segmentation, more flexible to data modeling and leading to better characterization and localization of object of interest in high-dimensional spaces since it is able to automatically reject irrelevant visual features. In order to determine adequately and automatically the number of regions in each image or frame, spatial information is incorporated as a prior information between neighboring pixels. Experimental results which are performed on a several real world images and videos demonstrate the effectiveness of the proposed framework with respect to other conventional Gaussian-based mixture models.


Image/video segmentation Visual features selection Mixture of bounded generalized Gaussian distributions Spatial information Minimum message length 


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Ines Channoufi
    • 1
    • 2
    Email author
  • Sami Bourouis
    • 1
    • 3
  • Nizar Bouguila
    • 4
  • Kamel Hamrouni
    • 1
  1. 1.Ecole Nationale d’Ingénieurs de Tunis, LR-SITI Laboratoire Signal Image et Technologies de l’InformationUniversité de Tunis El ManarTunisTunisia
  2. 2.ESPRIT School of EngineeringTunisTunisia
  3. 3.Taif UniversityTaifSaudi Arabia
  4. 4.The Concordia Institute for Information Systems Engineering (CIISE)Concordia UniversityMontrealCanada

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