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Color object segmentation and tracking using flexible statistical model and level-set

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Abstract

This study presents an unsupervised novel algorithm for color image segmentation, object detection and tracking based on unsupervised learning step followed with a post processing step implemented with a variational active contour. Flexible learning method of a finite mixture of bounded generalized Gaussian distributions using the Minimum Message Length (MML) principle is developed to cope with the complexity of color images modeling. We deal here simultaneously with the issues of data-model fitting, determining automatically the optimal number of classes and selecting relevant features. Indeed, a feature selection step based on MML is implemented to eliminate uninformative features and therefore improving the algorithm’s performance. For model’s parameters estimation, the maximum likelihood (ML) was investigated and conducted via expectation maximization (EM) algorithm. The obtained object boundaries in the first step are tracked on each frame of a given sequence using a geometric level-set approach. The implementation has the advantage to help in improving the computational efficiency in high-dimensional spaces. We demonstrate the effectiveness of the developed segmentation method through several experiments. Obtained results reveal that our approach is able to achieve higher precision as compared to several other methods for color image segmentation and object tracking.

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Acknowledgments

Taif University Researchers Supporting Project number (TURSP-2020/26), Taif University, Taif, Saudi Arabia.

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Authors and Affiliations

  1. Department of Information Technology, College of Computers and Information Technology, Taif University, Taif, P.O. Box 11099, Taif, 21944, Saudi Arabia

    Sami Bourouis & Roobaea Alroobaea

  2. LR-SITI Laboratoire Signal Image et Technologies de l’Information, Université de Tunis El Manar, Tunis, 1002, Tunisia

    Sami Bourouis & Ines Channoufi

  3. Department of Industrial and Systems Engineering, Faculty of Engineering, University of Jeddah, Jeddah, Saudi Arabia

    Saeed Rubaiee & Murad Andejany

  4. The Concordia Institute for Information Systems Engineering (CIISE), Concordia University, Montreal, QC H3G 1T7, Canada

    Nizar Bouguila

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  1. Sami Bourouis
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  2. Ines Channoufi
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  3. Roobaea Alroobaea
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  4. Saeed Rubaiee
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  6. Nizar Bouguila
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Correspondence to Sami Bourouis.

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Bourouis, S., Channoufi, I., Alroobaea, R. et al. Color object segmentation and tracking using flexible statistical model and level-set. Multimed Tools Appl 80, 5809–5831 (2021). https://doi.org/10.1007/s11042-020-09809-2

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