Multilevel image thresholding using entropy of histogram and recently developed population-based metaheuristic algorithms

Mousavirad, Seyed Jalaleddin; Ebrahimpour-Komleh, Hossein

doi:10.1007/s12065-017-0152-y

Cited by 65 publications

(24 citation statements)

References 63 publications

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“…Table 12 gives the Chi-square (χ 2 ) value and p-value of Friedman test at different threshold levels. According to the Chi-square distribution table, the critical value for 5 (6 algorithm -1) degrees of freedom at 0.05 significant level is 11.07 [57]. It can be observed from the table that the Chi-square values obtained are much larger than the critical value and the p-values obtained are much smaller than the significant level.…”

Section: Statistical Testmentioning

confidence: 88%

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Dynamic Harris Hawks Optimization with Mutation Mechanism for Satellite Image Segmentation

et al. 2019

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In this paper, a novel satellite image segmentation technique based on dynamic Harris hawks optimization with a mutation mechanism (DHHO/M) is proposed. Compared with the original Harris hawks optimization (HHO), the dynamic control parameter strategy and mutation operator used in DHHO/M can avoid falling into the local optimum and efficiently enhance the search capability. To evaluate the performance of the proposed method, a series of experiments are carried out on various satellite images. Eight advanced thresholding approaches are selected for comparison. Three criteria are adopted to determine the segmentation thresholds, namely Kapur’s entropy, Tsallis entropy, and Otsu between-class variance. Furthermore, four oil pollution images are used to further assess the practicality and feasibility of the proposed method on real engineering problem. The experimental results illustrate that the DHHO/M based thresholding technique is superior to others in the following three aspects: fitness function evaluation, image segmentation effect, and statistical tests.

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Section: Statistical Testmentioning

confidence: 88%

“…In this section, all available thresholding approaches are compared at K=10, 15, and 20 to assess the performance on high dimensional tasks. Threshold levels selected are the same as [57]. The average fitness function values obtained are presented in Table 14 (bold is the best).…”

Section: Search Capability On High Dimensional Problemsmentioning

confidence: 99%

Dynamic Harris Hawks Optimization with Mutation Mechanism for Satellite Image Segmentation

et al. 2019

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“…The chi-square χ 2 value and p-value are presented in Table 10. According to the chi-square distribution table, the critical value for 7 (8 algorithms − 1) degrees of freedom with 5% significant level is 14.067 [67,68]. As shown in Table 10, the chi-square values obtained at all threshold levels were much larger than the critical value, and the p-values acquired for all number of thresholds were far less than 0.05.…”

Section: Statistical Testsmentioning

confidence: 94%

Hybrid Grasshopper Optimization Algorithm and Differential Evolution for Multilevel Satellite Image Segmentation

et al. 2019

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An efficient satellite image segmentation method based on a hybrid grasshopper optimization algorithm (GOA) and minimum cross entropy (MCE) is proposed in this paper. The proposal is known as GOA–jDE, and it merges GOA with self-adaptive differential evolution (jDE) to improve the search efficiency, preserving the population diversity especially in the later iterations. A series of experiments is conducted on various satellite images for evaluating the performance of the algorithm. Both low and high levels of the segmentation are taken into account, increasing the dimensionality of the problem. The proposed approach is compared with the standard color image thresholding methods, as well as the advanced satellite image thresholding techniques based on different criteria. Friedman test and Wilcoxon’s rank sum test are performed to assess the significant difference between the algorithms. The superiority of the proposed method is illustrated from different aspects, such as average fitness function value, peak signal to noise ratio (PSNR), structural similarity index (SSIM), feature similarity index (FSIM), standard deviation (STD), convergence performance, and computation time. Furthermore, natural images from the Berkeley segmentation dataset are also used to validate the strong robustness of the proposed method.

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“…The Friedman test is a non-parametric statistical hypothesis test. It is applied to investigate whether there is a meaningful statistical difference among the results, and to compare and validate the performance of algorithms [65]. Due to stochastic and random characteristics of CCTC1, CCTC2, and CCRL algorithms, this statistical test is required.…”

Section: Evaluation Of Behavioural Conformance Checkingmentioning

confidence: 99%

“…Calculation of ranking is the first step in the Friedman test. Average ranking is applied to rank each algorithm, and equal values are given to the average ranks [65]. Table 8 shows the ranks for each of the employed algorithms.…”

Section: Evaluation Of Behavioural Conformance Checkingmentioning

confidence: 99%

Conformance Checking of the Activity Network with the Social Relationships Structure in the Context of Social Commerce

Esmaeili

Golpayegani

2020

J. theor. appl. electron. commer. res.

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Control and management of e-markets are one of the essential needs in social commerce context. Social commerce systems include not only informational flows, financial and goods/services but also social relationships and interactions. The focus of this research is to find the meaningful relationship between social relations and the network of various commercial flows among human resources (i.e., activity network) in social commerce context. If the activity network conforms with social relations, then business processes and, in general, the e-market can be steered by social relations. The proposed research framework has been constructed from a combination of process mining concepts, multi-layered network modeling, network analyzing, and social theories. Such a combination in the conformance checking field is an original contribution of this study, not yet explored. Two simulated sample datasets in four different versions are applied for the evaluation. Furthermore, Friedman's statistical test is employed to proof of the research hypothesis and gain more confidence in the obtained results. The results indicate that, despite the structural mismatch of the social network with the activity network, social relations affect the formation of business interactions among human resources. This effect is also different for various flow types. Multi-layered network modeling demonstrates this difference.

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Multilevel image thresholding using entropy of histogram and recently developed population-based metaheuristic algorithms

Cited by 65 publications

References 63 publications

Dynamic Harris Hawks Optimization with Mutation Mechanism for Satellite Image Segmentation

Dynamic Harris Hawks Optimization with Mutation Mechanism for Satellite Image Segmentation

Hybrid Grasshopper Optimization Algorithm and Differential Evolution for Multilevel Satellite Image Segmentation

Conformance Checking of the Activity Network with the Social Relationships Structure in the Context of Social Commerce

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