A hybrid differential evolution for optimal multilevel image thresholding

Mlakar, Uroš; Potočnik, Božidar; Brest, Janez

doi:10.1016/j.eswa.2016.08.046

Cited by 113 publications

(29 citation statements)

References 20 publications

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“…It is worth noting that all images are resized, and the height is fixed to 481 pixels. Both state-of-the-art and conventional multilevel color image segmentation techniques are employed, such as the standard GOA and DE, modified GOA (MGOA) [55], hybrid jDE (hjDE) [21], βDE (BDE) [56], bat algorithm (BA) [17], and particle swarm optimization (PSO) [15]. For the second experiment, the GOA-jDE-based method using MCE (GOA-jDE-MCE) with other threshold-based satellite image segmentation methods, such as modified artificial bee colony using Tsallis entropy (MABC-Tsallis) [57], improved the differential search algorithm using Otsu between-class variance (IDSA-Otsu) [58], and cuckoo search using MCE (CS-MCE) [13].…”

Section: Methodsmentioning

confidence: 99%

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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Section: Methodsmentioning

confidence: 99%

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

et al. 2019

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“…Following (Wang et al, 2004), c 1 and c 2 are two constants, such that c 1 = 6.5025 and c 2 = 58.52252 (Mlakar et al, 2016). The highest value of SSIM indicates a better performance.…”

Section: Segmented Image Quality Metricsmentioning

confidence: 96%

Whale Optimization Algorithm and Moth-Flame Optimization for multilevel thresholding image segmentation

Elaziz

Ewees

Hassanien

2017

Expert Systems with Applications

497

189

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“…All these will affect the efficiency of the algorithm, which will lead to inefficiency of the algorithm and increase the amount of computation in segmentation. Therefore, the accurate segmentation of satellite images is a very challenging task [5]. Ten satellite images are selected for segmentation to achieve better contrast effect.…”

Section: Satellite Color Image Usedmentioning

confidence: 99%

“…It is necessary to locate objects and boundaries accurately in satellite images. Therefore, color satellite image segmentation is a critical and challenging topic [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Masi Entropy for Satellite Color Image Segmentation Using Tournament-Based Lévy Multiverse Optimization Algorithm

et al. 2019

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A novel multilevel threshold segmentation method for color satellite images based on Masi entropy is proposed in this paper. Lévy multiverse optimization algorithm (LMVO) has a strong advantage over the traditional multiverse optimization algorithm (MVO) in finding the optimal solution for the segmentation in the three channels of an RGB image. As the work advancement introduces a Lévy multiverse optimization algorithm which uses tournament selection instead of roulette wheel selection, and updates some formulas in the algorithm with mutation factor. Then, the proposal is called TLMVO, and another advantage is that the population diversity of the algorithm in the latest iterations is maintained. The Masi entropy is used as an application and combined with the improved TLMVO algorithm for satellite color image segmentation. Masi entropy combines the additivity of Renyi entropy and the non-extensibility of Tsallis entropy. By increasing the number of thesholds, the quality of segmenttion becomes better, then the dimensionality of the problem also increases. Fitness function value, average CPU running time, Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM) and Feature Similarity Index (FSIM) were used to evaluate the segmentation results. Further statistical evaluation was given by Wilcoxon's rank sum test and Friedman test. The experimental results show that the TLMVO algorithm has wide adaptability to high-dimensional optimization problems, and has obvious advantages in objective function value, image quality detection, convergence performance and robustness.

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A hybrid differential evolution for optimal multilevel image thresholding

Cited by 113 publications

References 20 publications

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

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

Whale Optimization Algorithm and Moth-Flame Optimization for multilevel thresholding image segmentation

Masi Entropy for Satellite Color Image Segmentation Using Tournament-Based Lévy Multiverse Optimization Algorithm

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