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Variational Mode Decomposition-Based Multilevel Threshold Selection Scheme for Color Image Segmentation

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Abstract

Image segmentation is a method of subdividing an image into numerous meaningful regions or objects, which makes the image more informative and easy to analyze. Thresholding-based approaches are extensively employed for segmenting the image due to their low computational cost and are easy implementation. However, histogram-based thresholding schemes suffer from high variation that leads to abnormalities and sharp specifics. In this paper, we propose a technique for multilevel color image segmentation through variational mode decomposition (VMD) and Kapur’s entropy. Initially, the VMD is employed in order to decompose the histogram into corresponding submodes of analysis and attributes extraction, which leads to the removal of the unfavorable effects. Then, Kapur’s entropy is incorporated in order to generate accurate and optimal thresholds for segmentation. For the performance evaluation of the presented VMD–Kapur algorithm, various qualitative metrics have been used such as probability rand index, mean-square error, peak signal-to-noise ratio, variation of information, structural similarity index, dice error, feature similarity index, entropy, Jaccard/Tanimoto error, and normalized absolute error. The experimental results show that the proposed technique produces best-segmented images compared to Kapur’s, Tsallis, Masi, and fuzzy entropies.

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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

  2. Department of Electronics and Communication Engineering, International Institute of Information Technology, Naya Raipur, India

    Anurag Singh

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  1. Neha Singh
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  2. Ashish Kumar Bhandari
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Singh, N., Bhandari, A.K. & Singh, A. Variational Mode Decomposition-Based Multilevel Threshold Selection Scheme for Color Image Segmentation. Circuits Syst Signal Process 39, 3978–4020 (2020). https://doi.org/10.1007/s00034-020-01349-2

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