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Backtracking search algorithm for color image multilevel thresholding

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Abstract

Multilevel thresholding of the color images such as natural and satellite images becomes a challenging task due to the inherent fuzziness and ambiguity in such images. To address this issue, a modified fuzzy entropy (MFE) function is proposed in this paper. MFE function is the difference of adjacent entropies, which is optimized to provide thresholding levels such that all regions have almost equal entropies. To improve the performance of MFE, backtracking search algorithm is used. The numerical and statistical results indicate that MFE-BSA has higher peak signal-to-noise ratio, lower mean square error for all the images at different thresholding levels. Moreover, structural and feature similarity indices for MFE-BSA are closer to unity and the average fitness value obtained using MFE-BSA is minimum (lesser than 0.5). Overall, MFE-BSA shows very good segmentation results in terms of preciseness, robustness, and stability.

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Author notes

  1. A. Kumar

    Present address: School of Electrical Engineering and Computer Science, GIST, Gwangju, Korea

Authors and Affiliations

  1. PDPM Indian Institute of Information Technology, Design and Manufacturing, Jabalpur, 482005, India

    S. Pare, A. K. Bhandari, A. Kumar & V. Bajaj

  2. National Institute of Technology, Patna, 800005, India

    A. K. Bhandari

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  1. S. Pare
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  2. A. K. Bhandari
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  3. A. Kumar
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  4. V. Bajaj
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Correspondence to A. K. Bhandari.

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Pare, S., Bhandari, A.K., Kumar, A. et al. Backtracking search algorithm for color image multilevel thresholding. SIViP 12, 385–392 (2018). https://doi.org/10.1007/s11760-017-1170-z

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  • DOI: https://doi.org/10.1007/s11760-017-1170-z

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