Multi-level thresholding based on differential evolution and Tsallis Fuzzy entropy

Raj, Aditya; Gautam, Gunjan; Abdullah, Siti Norul Huda Sheikh; Zaini, Abbas Salimi; Mukhopadhyay, Susanta

doi:10.1016/j.imavis.2019.07.004

Cited by 14 publications

(4 citation statements)

References 21 publications

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“…In this approach, the solutions were evaluated by using the Otsu and Kapur as fitness functions. Whereas, the differential evolution and Tsallis Fuzzy entropy is proposed an image segmentation method in [57].…”

Section: Experimental Series 2: Image Segmentationmentioning

confidence: 99%

Fractional-order calculus-based flower pollination algorithm with local search for global optimization and image segmentation

Yousri

Elaziz

Mirjalili

2020

Knowledge-Based Systems

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Section: Experimental Series 2: Image Segmentationmentioning

confidence: 99%

Fractional-order calculus-based flower pollination algorithm with local search for global optimization and image segmentation

Yousri

Elaziz

Mirjalili

2020

Knowledge-Based Systems

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“…They specified that the EMA has better performance than compared algorithms on different metrics. Raj et al (Raj et al, 2019) employed differential evolution (DE) algorithm with Tsallis-Fuzzy entropy method for an image segmentation problem. The performance of the Tsallis-Fuzzy approach was compared with the Shannon and Tsallis methods.…”

Section: Introductionmentioning

confidence: 99%

The Comparison of the Effects of Thresholding Methods on Segmentation Using the Moth Flame Optimization Algorithm

Karakoyun

2023

Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi

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Segmentation is an important preprocessing step that directly affects the success in image processing applications. There are many methods and approaches used for the segmentation process. Thresholding is a frequently used approach among these methods. There are several suggested approaches to thresholding. In this study, six different thresholding approaches were used as the fitness functions using the moth flame algorithm and the results obtained from these approaches were compared. In experimental studies, seven different threshold levels of 10 different images were studied. In comparisons made with three different metrics, it was seen that the Otsu method was generally more successful. It has also been observed that the minimum cross entropy and Renyi entropies can be used as alternatives.

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“…compared with the standard GWO, EO and DE algorithm, it showed better performance in the optimal objective function and threshold stability. Raj [17] used DE method to realize image thresholding, and takes PSNR, SSIM and SNR as evaluation indices. Compared with BFO, bees' algorithm and their improved algorithms, it has better effect in standard deviation of objective function and computing efficiency.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Hybrid Coyote Optimization Algorithm for Image Thresholding

Li¹,

Huang²,

Qian³

et al. 2022

Computers, Materials &Amp; Continua

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In order to address the problems of Coyote Optimization Algorithm in image thresholding, such as easily falling into local optimum, and slow convergence speed, a Fuzzy Hybrid Coyote Optimization Algorithm (hereinafter referred to as FHCOA) based on chaotic initialization and reverse learning strategy is proposed, and its effect on image thresholding is verified. Through chaotic initialization, the random number initialization mode in the standard coyote optimization algorithm (COA) is replaced by chaotic sequence. Such sequence is nonlinear and long-term unpredictable, these characteristics can effectively improve the diversity of the population in the optimization algorithm. Therefore, in this paper we first perform chaotic initialization, using chaotic sequence to replace random number initialization in standard COA. By combining the lens imaging reverse learning strategy and the optimal worst reverse learning strategy, a hybrid reverse learning strategy is then formed. In the process of algorithm traversal, the best coyote and the worst coyote in the pack are selected for reverse learning operation respectively, which prevents the algorithm falling into local optimum to a certain extent and also solves the problem of premature convergence. Based on the above improvements, the coyote optimization algorithm has better global convergence and computational robustness. The simulation results show that the algorithm has better thresholding effect than the five commonly used optimization algorithms in image thresholding when multiple images are selected and different threshold numbers are set.

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Multi-level thresholding based on differential evolution and Tsallis Fuzzy entropy

Cited by 14 publications

References 21 publications

Fractional-order calculus-based flower pollination algorithm with local search for global optimization and image segmentation

Fractional-order calculus-based flower pollination algorithm with local search for global optimization and image segmentation

The Comparison of the Effects of Thresholding Methods on Segmentation Using the Moth Flame Optimization Algorithm

Fuzzy Hybrid Coyote Optimization Algorithm for Image Thresholding

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