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A chimp-inspired remora optimization algorithm for multilevel thresholding image segmentation using cross entropy

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Abstract

Multilevel thresholding is one of the most commonly used methods in image segmentation. However, the exhaustive search methods are costly in determining optimal thresholds and the conventional remora optimization algorithm (ROA) is prone to the premature convergence. This paper presents a chimp-inspired remora optimization algorithm (HCROA) to search optimal threshold levels, and the cross-entropy is employed as the objective function. In HCROA, the particles’ position are adjusted by the Chimp Optimization Algorithm (ChOA) because of its good exploitation ability and sufficient diversity. With this change, HCROA achieves both the intra-group diversity intelligence and a suitable balance between exploration and exploitation. To validate its performance, a series of experiments are performed. First, we test the HCROA’s segmentation accuracy by a set of natural gray-scale images with different thresholds. Second, HCROA is implemented for noisy image segmentation to evaluate its robustness. Several reference-based measurements including Peak Signal-to-Noise Ratio (PSNR), Structural Similarity (SSIM), Feature Similarity (FSIM), Quality Index based on Local Variance (QILV), Haar wavelet-based Perceptual Similarity Index (HPSI), Wilcoxon test, and CPU time have been considered for evaluating the proposed method. Additionally, eight well-known predecessors are injected for parallel comparison. The comparison results prove that the suggested method outperforms the existing approaches in terms of accuracy, convergence speed, noise robustness, and efficiency.

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Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

This work is supported by the Hainan Provincial Natural Science Foundation of China (2019RC176, 621RC511), and the National Natural Science Foundation of China (11861030). The authors would like to thank the support of the State Key Laboratory of Marine Resource Utilization in the South China Sea at Hainan University.

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Authors and Affiliations

  1. School of Computer Science and Technology, Hainan University, Haikou, 570228, China

    Qingxin Liu & Qi Qi

  2. School of Mathematics and Statistics, Hainan Normal University, Haikou, 571158, China

    Ni Li

  3. Key Laboratory of Data Science and Intelligence Education of Ministry of Education, Hainan Normal University, Haikou, 571158, China

    Ni Li

  4. School of Information Engineering, Sanming University, Sanming, 365004, China

    Heming Jia

  5. Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, 19328, Jordan

    Laith Abualigah

  6. Faculty of Information Technology, Middle East University, Amman, 11831, Jordan

    Laith Abualigah

Authors
  1. Qingxin Liu
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  2. Ni Li
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  3. Heming Jia
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  4. Qi Qi
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  5. Laith Abualigah
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Contributions

Conceptualization, QL, NL, QQ: Methodology, QL, NL: Software, QL: Visualization, QL: Writing-original draft, QL: Writing–review & editing, NL, HJ, QQ, LA: Funding acquisition, NL, QQ: Validation, QQ: Investigation, QQ: Supervision, QQ. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Qi Qi.

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Appendices

Appendix 1

See Tables 6, 7, 8, 9, 10, 11 and 12, Fig. 8.

Table 6 The best fitness values obtained by algorithms over all images
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Table 7 The PSNR values obtained by algorithms over all images
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Table 8 The SSIM values obtained by algorithms over all images
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Table 9 The FSIM values obtained by algorithms over all images
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Table 10 The QILV values obtained by algorithms over all images
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Table 11 The HPSI values obtained by algorithms over all images
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Table 12 The p values obtained by algorithms over all images
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Fig. 8
figure 8figure 8figure 8

The segmented images and corresponding Jet colormap produced by HCROA

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Appendix 2

See Tables 13, 14, 15, 16, 17, 18 and 19.

Table 13 The best fitness values obtained by algorithms over all images
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Table 14 The PSNR values obtained by algorithms over all images
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Table 15 The SSIM values obtained by algorithms over all images
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Table 16 The FSIM values obtained by algorithms over all images
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Table 17 The QILV values obtained by algorithms over all images
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Table 18 The HPSI values obtained by algorithms over all images
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Table 19 The p values obtained by algorithms over all images
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Liu, Q., Li, N., Jia, H. et al. A chimp-inspired remora optimization algorithm for multilevel thresholding image segmentation using cross entropy. Artif Intell Rev 56 (Suppl 1), 159–216 (2023). https://doi.org/10.1007/s10462-023-10498-0

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