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Multiclass variance based variational decomposition system for image segmentation

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Abstract

Thresholding-based approaches are widely used for image segmentation due to their low computational cost and complexity and ease of implementation. We proposed a novel framework for performing multilevel image segmentation. The present paper uses the feature of variational mode decomposition (VMD) and multiclass variance function for segmentation. The histogram-based thresholding schemes suffer from high fluctuation that leads to abnormalities and sharp specifics. VMD is applied to remove the unfavorable effects of the histogram by decomposing it into corresponding sub-modes for analysis and extraction of attributes. The Otsu function is then incorporated to generate accurate and optimal threshold values for subdivisions to meet the desire image segmentation. The experimental outcomes show that the proposed technique produces improved segmented images as compared to the other state-of-the-art techniques.

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Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, National Institute of Technology Patna, Patna, 800005, India

    Neha Singh & Ashish Kumar Bhandari

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  1. Neha Singh
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  2. Ashish Kumar Bhandari
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Correspondence to Ashish Kumar Bhandari.

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Singh, N., Bhandari, A.K. Multiclass variance based variational decomposition system for image segmentation. Multimed Tools Appl 82, 41609–41639 (2023). https://doi.org/10.1007/s11042-023-14593-w

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