How effective are current population-based metaheuristic algorithms for variance-based multi-level image thresholding?

Mousavirad, Seyed Jalaleddin; Schaefer, Gerald; Zhou, Huiyu; Moghadam, Mahshid Helali

doi:10.1016/j.knosys.2023.110587

Cited by 4 publications

(3 citation statements)

References 75 publications

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“…In multilevel image thresholding, the computation time increases exponentially as the number of thresholds increases. To effectively reduce the computation time, many methods apply evolutionary computation algorithms to solve this problem in image multilevel thresholding 15 – 20 , 31 , 32 . Since PSO has excellent performance in solving combinatorial optimization problems, it has been successfully applied to multilevel image thresholding 15 , 33 .…”

Section: Proposed Methodologymentioning

confidence: 99%

“…As the number of thresholds increases, the computation time also increases exponentially. Many studies have incorporated swarm intelligence optimization algorithms into image multilevel thresholding problems to solve this problem 15 – 20 . Segmentation is a multi-constraint optimization problem when using swarm intelligence optimization algorithms for multilevel thresholding.…”

Section: Introductionmentioning

confidence: 99%

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Multilevel thresholding with divergence measure and improved particle swarm optimization algorithm for crack image segmentation

Nie,

Liu,

Zhang

2024

Sci Rep

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Crack formation is a common phenomenon in engineering structures, which can cause serious damage to the safety and health of these structures. An important method of ensuring the safety and health of engineered structures is the prompt detection of cracks. Image threshold segmentation based on machine vision is a crucial technology for crack detection. Threshold segmentation can separate the crack area from the background, providing convenience for more accurate measurement and evaluation of the crack condition and location. The segmentation of cracks in complex scenes is a challenging task, and this goal can be achieved by means of multilevel thresholding. The arithmetic-geometric divergence combines the advantages of the arithmetic mean and the geometric mean in probability measures, enabling a more precise capture of the local features of an image in image processing. In this paper, a multilevel thresholding method for crack image segmentation based on the minimum arithmetic-geometric divergence is proposed. To address the issue of time complexity in multilevel thresholding, an enhanced particle swarm optimization algorithm with local stochastic perturbation is proposed. In crack detection, the thresholding criterion function based on the minimum arithmetic-geometric divergence can adaptively determine the thresholds according to the distribution characteristics of pixel values in the image. The proposed enhanced particle swarm optimization algorithm can increase the diversity of candidate solutions and enhance the global convergence performance of the algorithm. The proposed method for crack image segmentation is compared with seven state-of-the-art multilevel thresholding methods based on several metrics, including RMSE, PSNR, SSIM, FSIM, and computation time. The experimental results show that the proposed method outperforms several competing methods in terms of these metrics.

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Section: Proposed Methodologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multilevel thresholding with divergence measure and improved particle swarm optimization algorithm for crack image segmentation

Nie,

Liu,

Zhang

2024

Sci Rep

View full text Add to dashboard Cite

show abstract

“…These algorithms, inspired by natural phenomena and artificial intelligence, are adept at tackling diverse and intricate optimization challenges [41,42]. Algorithms such as genetic algorithms, Whale Optimization Algorithm (WOA), Particle Swarm Optimization (PSO), and Harris Hawks Optimization (HHO) have each contributed uniquely to threshold selection, each with distinct strengths and weaknesses [43][44][45][46][47][48][49][50][51][52][53][54]. Recent advancements include swarm intelligence algorithms for multi-threshold segmentation, particularly effective in processing COVID-19 chest X-rays and CT scans [55][56][57][58][59].…”

Section: Application Of Otsu Algorithm In Image Segmentationmentioning

confidence: 99%

Advancing image segmentation with DBO-Otsu: Addressing rubber tree diseases through enhanced threshold techniques

Xie,

Wu,

Tang

et al. 2024

PLoS ONE

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Addressing the profound impact of Tapping Panel Dryness (TPD) on yield and quality in the global rubber industry, this study introduces a cutting-edge Otsu threshold segmentation technique, enhanced by Dung Beetle Optimization (DBO-Otsu). This innovative approach optimizes the segmentation threshold combination by accelerating convergence and diversifying search methodologies. Following initial segmentation, TPD severity levels are meticulously assessed using morphological characteristics, enabling precise determination of optimal thresholds for final segmentation. The efficacy of DBO-Otsu is rigorously evaluated against mainstream benchmarks like Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Feature Similarity Index (FSIM), and compared with six contemporary swarm intelligence algorithms. The findings reveal that DBO-Otsu substantially surpasses its counterparts in image segmentation quality and processing speed. Further empirical analysis on a dataset comprising TPD cases from level 1 to 5 underscores the algorithm’s practical utility, achieving an impressive 80% accuracy in severity level identification and underscoring its potential for TPD image segmentation and recognition tasks.

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Metaheuristic-based energy-aware image compression for mobile app development

Mousavirad,

Alexandre

2024

Multimed Tools Appl

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The widely applied JPEG standard has undergone recent efforts using population-based metaheuristic (PBMH) algorithms to optimise quantisation tables (QTs) for specific images. However, user preferences, like an Android developer’s preference for small-size images, are often overlooked, leading to high-quality images with large file sizes. Another limitation is the lack of comprehensive coverage in current QTs, failing to accommodate all possible combinations of file size and quality. Therefore, this paper aims to propose three distinct contributions. First, to include the user’s opinion in the compression process, the file size of the output image can be controlled by a user in advance. To this end, we propose a novel objective function for population-based JPEG image compression. Second, we suggest a novel representation to tackle the lack of comprehensive coverage. Our proposed representation can not only provide more comprehensive coverage but also find the proper value for the quality factor for a specific image without any background knowledge. Both representation and objective function changes are independent of the search strategies and can be used with any population-based metaheuristic (PBMH) algorithm. Therefore, as the third contribution, we also provide a comprehensive benchmark on 22 state-of-the-art and recently-introduced PBMH algorithms on our new formulation of JPEG image compression. Our extensive experiments on different benchmark images and in terms of different criteria show that our novel formulation for JPEG image compression can work effectively.

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How effective are current population-based metaheuristic algorithms for variance-based multi-level image thresholding?

Cited by 4 publications

References 75 publications

Multilevel thresholding with divergence measure and improved particle swarm optimization algorithm for crack image segmentation

Multilevel thresholding with divergence measure and improved particle swarm optimization algorithm for crack image segmentation

Advancing image segmentation with DBO-Otsu: Addressing rubber tree diseases through enhanced threshold techniques

Metaheuristic-based energy-aware image compression for mobile app development

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