scholarly journals Kapur’s Entropy for Color Image Segmentation Based on a Hybrid Whale Optimization Algorithm

Entropy ◽  
2019 ◽  
Vol 21 (3) ◽  
pp. 318 ◽  
Author(s):  
Chunbo Lang ◽  
Heming Jia
Keyword(s):  
Image Segmentation ◽  
Optimization Algorithm ◽  
Heuristic Algorithms ◽  
Color Image ◽  
Similarity Index ◽  
Whale Optimization Algorithm ◽  
Color Image Segmentation ◽  
De Algorithm ◽  
Whale Optimization ◽  
Kapur’S Entropy

In this paper, a new hybrid whale optimization algorithm (WOA) called WOA-DE is proposed to better balance the exploitation and exploration phases of optimization. Differential evolution (DE) is adopted as a local search strategy with the purpose of enhancing exploitation capability. The WOA-DE algorithm is then utilized to solve the problem of multilevel color image segmentation that can be considered as a challenging optimization task. Kapur’s entropy is used to obtain an efficient image segmentation method. In order to evaluate the performance of proposed algorithm, different images are selected for experiments, including natural images, satellite images and magnetic resonance (MR) images. The experimental results are compared with state-of-the-art meta-heuristic algorithms as well as conventional approaches. Several performance measures have been used such as average fitness values, standard deviation (STD), peak signal to noise ratio (PSNR), structural similarity index (SSIM), feature similarity index (FSIM), Wilcoxon’s rank sum test, and Friedman test. The experimental results indicate that the WOA-DE algorithm is superior to the other meta-heuristic algorithms. In addition, to show the effectiveness of the proposed technique, the Otsu method is used for comparison.

scholarly journals Intelligent Bio-Inspired Whale Optimization Algorithm for Color Image based Segmentation

2020 ◽  
Vol 28 (4) ◽  
Author(s):  
Athraa Jasim Mohammed ◽  
Khalil Ibrahim Ghathwan
Keyword(s):  
Optimization Algorithm ◽  
Heuristic Algorithms ◽  
Color Image ◽  
Clustering Algorithms ◽  
Color Space ◽  
Input Image ◽  
Whale Optimization Algorithm ◽  
Color Image Segmentation ◽  
Local Optima ◽  
Whale Optimization

Color image segmentation is widely used methods for searching of homogeneous regions to classify them into various groups. Clustering is one technique that is used for this purpose. Clustering algorithms have drawbacks such as the finding of optimum centers within a cluster and the trapping in local optima. Even though inspired meta-heuristic algorithms have been adopted to enhance the clustering performance, some algorithms still need improvements. Whale optimization algorithm (WOA) is recognized to be enough competition with common meta-heuristic algorithms, where it has an ability to obtain a global optimal solution and avoid local optima. In this paper, a new method for color image based segmentation is proposed based on using whale optimization algorithm in clustering. The proposed method is called the whale color image based segmentation (WhCIbS). It was used to divide the color image into a predefined number of clusters. The input image in RGB color space was converted into L*a*b color space. Comparison of the proposed WhCIbS method was performed with the wolf color image based segmentation, cuckoo color image based segmentation, bat color image based segmentation, and k-means color image based segmentation over four benchmark color images. Experimental results demonstrated that the proposed WhCIbS had higher value of PSNR and lower value of RMSR in most cases compared to other methods.

scholarly journals Dragonfly Algorithm with Opposition-Based Learning for Multilevel Thresholding Color Image Segmentation

Symmetry ◽  
2019 ◽  
Vol 11 (5) ◽  
pp. 716 ◽  
Author(s):  
Xiaoli Bao ◽  
Heming Jia ◽  
Chunbo Lang
Keyword(s):  
Image Segmentation ◽  
Heuristic Algorithms ◽  
Color Image ◽  
Similarity Index ◽  
Search Space ◽  
Color Image Segmentation ◽  
Computational Time ◽  
Multilevel Thresholding ◽  
Dragonfly Algorithm ◽  
Opposition Based Learning

Multilevel thresholding is a very active research field in image segmentation, and has been successfully used in various applications. However, the computational time will increase exponentially as the number of thresholds increases, and for color images which contain more information this is even worse. To overcome the drawback while maintaining segmentation accuracy, a modified version of dragonfly algorithm (DA) with opposition-based learning (OBLDA) for color image segmentation is proposed in this paper. The opposition-based learning (OBL) strategy simultaneously considers the current solution and the opposite solution, which are symmetrical in the search space. With the introduction of OBL, the proposed algorithm has a faster convergence speed and more balanced exploration–exploitation compared with the original DA. In order to clearly demonstrate the outstanding performance of the OBLDA, the proposed method is compared with seven state-of-the-art meta-heuristic algorithms, through experiments on 10 test images. The optimal threshold values are calculated by the maximization of between-class variance and Kapur’s entropy. Meanwhile, some indicators, including peak signal to noise ratio (PSNR), feature similarity index (FSIM), structure similarity index (SSIM), the average fitness values, standard deviation (STD), and computation time are used as evaluation criteria in the experiments. The promising results reveal that proposed method has the advantages of high accuracy and remarkable stability. Wilcoxon’s rank sum test and Friedman test are also performed to verify the superiority of OBLDA in a statistical way. Furthermore, various satellite images are also included for robustness testing. In conclusion, the OBLDA algorithm is a feasible and effective method for multilevel thresholding color image segmentation.

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

2019 ◽  
Vol 11 (8) ◽  
pp. 942 ◽  
Author(s):  
Jia ◽  
Peng ◽  
Song ◽  
Oliva ◽  
Lang ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Color Image ◽  
Fitness Function ◽  
Similarity Index ◽  
Optimal Solution ◽  
Population Diversity ◽  
Color Image Segmentation ◽  
Masi Entropy

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.

scholarly journals A Hybrid Preaching Optimization Algorithm Based on Kapur Entropy for Multilevel Thresholding Color Image Segmentation

Entropy ◽  
2021 ◽  
Vol 23 (12) ◽  
pp. 1599
Author(s):  
Bowen Wu ◽  
Liangkuan Zhu ◽  
Jun Cao ◽  
Jingyu Wang
Keyword(s):  
Image Segmentation ◽  
Time Delay ◽  
Optimization Algorithm ◽  
Color Image ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Color Image Segmentation ◽  
Multilevel Thresholding ◽  
Evolutionary State

Multilevel thresholding segmentation of color images plays an important role in many fields. The pivotal procedure of this technique is determining the specific threshold of the images. In this paper, a hybrid preaching optimization algorithm (HPOA) for color image segmentation is proposed. Firstly, the evolutionary state strategy is adopted to evaluate the evolutionary factors in each iteration. With the introduction of the evolutionary state, the proposed algorithm has more balanced exploration-exploitation compared with the original POA. Secondly, in order to prevent premature convergence, a randomly occurring time-delay is introduced into HPOA in a distributed manner. The expression of the time-delay is inspired by particle swarm optimization and reflects the history of previous personal optimum and global optimum. To better verify the effectiveness of the proposed method, eight well-known benchmark functions are employed to evaluate HPOA. In the interim, seven state-of-the-art algorithms are utilized to compare with HPOA in the terms of accuracy, convergence, and statistical analysis. On this basis, an excellent multilevel thresholding image segmentation method is proposed in this paper. Finally, to further illustrate the potential, experiments are respectively conducted on three different groups of Berkeley images. The quality of a segmented image is evaluated by an array of metrics including feature similarity index (FSIM), peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and Kapur entropy values. The experimental results reveal that the proposed method significantly outperforms other algorithms and has remarkable and promising performance for multilevel thresholding color image segmentation.

scholarly journals Spatial Prediction of Landslide Susceptibility Using GIS-Based Data Mining Techniques of ANFIS with Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO)

2019 ◽  
Vol 9 (18) ◽  
pp. 3755 ◽  
Author(s):  
Wei Chen ◽  
Haoyuan Hong ◽  
Mahdi Panahi ◽  
Himan Shahabi ◽  
Yi Wang ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
Landslide Susceptibility ◽  
Heuristic Algorithms ◽  
Whale Optimization Algorithm ◽  
Economic Losses ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Prediction Ability ◽  
Inference System ◽  
Whale Optimization

The most dangerous landslide disasters always cause serious economic losses and human deaths. The contribution of this work is to present an integrated landslide modelling framework, in which an adaptive neuro-fuzzy inference system (ANFIS) is combined with the two optimization algorithms of whale optimization algorithm (WOA) and grey wolf optimizer (GWO) at Anyuan County, China. It means that WOA and GWO are used as two meta-heuristic algorithms to improve the prediction performance of the ANFIS-based methods. In addition, the step-wise weight assessment ratio analysis (SWARA) method is used to obtain the initial weight of each class of landslide influencing factors. To validate the effectiveness of the proposed framework, 315 landslide events in history were selected for our experiments and were randomly divided into the training and verification sets. To perform landslide susceptibility mapping, fifteen geological, hydrological, geomorphological, land cover, and other factors are considered for the modelling construction. The landslide susceptibility maps by SWARA, SWARA-ANFIS, SWARA-ANFIS-PSO, SWARA-ANFIS-WOA, and SWARA-ANFIS-GWO models are assessed using the measures of the receiver operating characteristic (ROC) curve and root-mean-square error (RMSE). The experiments demonstrated that the obtained results of modelling process from the SWARA to the SAWRA-ANFIS-GWO model were more accurate and that the proposed methods have satisfactory prediction ability. Specifically, prediction accuracy by area under the curve (AUC) of SWARA, SWARA-ANFIS, SWARA-ANFIS-PSO, SWARA-ANFIS-GWO, and SWARA-ANFIS-WOA models were 0.831, 0.831, 0.850, 0.856, and 0.869, respectively. Due to adaptability and usability, the proposed prediction methods can be applied to other areas for landslide management and mitigation as well as prevention throughout the world.

Improved whale optimization algorithm based on random hopping update and random control parameter

2021 ◽  
Vol 40 (1) ◽  
pp. 363-379
Author(s):  
Yanju Guo ◽  
Huan Shen ◽  
Lei Chen ◽  
Yu Liu ◽  
Zhilong Kang
Keyword(s):  
Optimization Algorithm ◽  
Control Parameter ◽  
Heuristic Algorithms ◽  
Whale Optimization Algorithm ◽  
Original Algorithm ◽  
Multimodal Functions ◽  
Whale Optimization ◽  
Update Strategy ◽  
Random Control ◽  
Better Than

Whale Optimization Algorithm (WOA) is a relatively novel algorithm in the field of meta-heuristic algorithms. WOA can reveal an efficient performance compared with other well-established optimization algorithms, but there is still a problem of premature convergence and easy to fall into local optimal in complex multimodal functions, so this paper presents an improved WOA, and proposes the random hopping update strategy and random control parameter strategy to improve the exploration and exploitation ability of WOA. In this paper, 24 well-known benchmark functions are used to test the algorithm, including 10 unimodal functions and 14 multimodal functions. The experimental results show that the convergence accuracy of the proposed algorithm is better than that of the original algorithm on 21 functions, and better than that of the other 5 algorithms on 23 functions.

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