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On Methods for Merging Mixture Model Components Suitable for Unsupervised Image Segmentation Tasks

Author

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  • Branislav Panić

    (Faculty of Mechanical Engineering, University of Ljubljana, Aškerčeva Ulica 6, 1000 Ljubljana, Slovenia)

  • Marko Nagode

    (Faculty of Mechanical Engineering, University of Ljubljana, Aškerčeva Ulica 6, 1000 Ljubljana, Slovenia)

  • Jernej Klemenc

    (Faculty of Mechanical Engineering, University of Ljubljana, Aškerčeva Ulica 6, 1000 Ljubljana, Slovenia)

  • Simon Oman

    (Faculty of Mechanical Engineering, University of Ljubljana, Aškerčeva Ulica 6, 1000 Ljubljana, Slovenia)

Abstract

Unsupervised image segmentation is one of the most important and fundamental tasks in many computer vision systems. Mixture model is a compelling framework for unsupervised image segmentation. A segmented image is obtained by clustering the pixel color values of the image with an estimated mixture model. Problems arise when the selected optimal mixture model contains a large number of mixture components. Then, multiple components of the estimated mixture model are better suited to describe individual segments of the image. We investigate methods for merging the components of the mixture model and their usefulness for unsupervised image segmentation. We define a simple heuristic for optimal segmentation with merging of the components of the mixture model. The experiments were performed with gray-scale and color images. The reported results and the performed comparisons with popular clustering approaches show clear benefits of merging components of the mixture model for unsupervised image segmentation.

Suggested Citation

  • Branislav Panić & Marko Nagode & Jernej Klemenc & Simon Oman, 2022. "On Methods for Merging Mixture Model Components Suitable for Unsupervised Image Segmentation Tasks," Mathematics, MDPI, vol. 10(22), pages 1-22, November.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:22:p:4301-:d:974928
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    References listed on IDEAS

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    1. Branislav Panić & Marko Nagode & Jernej Klemenc & Simon Oman, 2023. "Combining Color and Spatial Image Features for Unsupervised Image Segmentation with Mixture Modelling and Spectral Clustering," Mathematics, MDPI, vol. 11(23), pages 1-22, November.

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