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 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 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 Modified Artificial Ecosystem-Based Optimization for Multilevel Thresholding Image Segmentation

Mathematics ◽  
2021 ◽  
Vol 9 (19) ◽  
pp. 2363
Author(s):  
Ahmed A. Ewees ◽  
Laith Abualigah ◽  
Dalia Yousri ◽  
Ahmed T. Sahlol ◽  
Mohammed A. A. Al-qaness ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Differential Evolution ◽  
High Performance ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Main Idea ◽  
Structural Similarity ◽  
Multilevel Thresholding ◽  
Artificial Ecosystem ◽  
Segmentation Methods

Multilevel thresholding is one of the most effective image segmentation methods, due to its efficiency and easy implementation. This study presents a new multilevel thresholding method based on a modified artificial ecosystem-based optimization (AEO). The differential evolution (DE) is applied to overcome the shortcomings of the original AEO. The main idea of the proposed method, artificial ecosystem-based optimization differential evolution (AEODE), is to employ the operators of the DE as a local search of the AEO to improve the ecosystem of solutions. We used benchmark images to test the performance of the AEODE, and we compared it to several existing approaches. The proposed AEODE achieved a high performance when evaluated by the structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), and fitness values. Moreover, the AEODE outperformed the basic version of the AEO concerning SSIM and PSNR by 78% and 82%, respectively, which reserves the best features for each of AEO and DE.

scholarly journals Multiverse Optimization Algorithm Based on Lévy Flight Improvement for Multithreshold Color Image Segmentation

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 32805-32844 ◽  
Author(s):  
Heming Jia ◽  
Xiaoxu Peng ◽  
Wenlong Song ◽  
Chunbo Lang ◽  
Zhikai Xing ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Optimization Algorithm ◽  
Color Image ◽  
Color Image Segmentation ◽  
Lévy Flight ◽  
Levy Flight

scholarly journals Color Image Generation from Range and Reflection Data of LiDAR

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5414
Author(s):  
Hyun-Koo Kim ◽  
Kook-Yeol Yoo ◽  
Ho-Youl Jung
Keyword(s):  
Color Image ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Point Clouds ◽  
Range Data ◽  
Fusion Method ◽  
Image Generation ◽  
Learning Networks ◽  
Reflection Data

Recently, it has been reported that a camera-captured-like color image can be generated from the reflection data of 3D light detection and ranging (LiDAR). In this paper, we present that the color image can also be generated from the range data of LiDAR. We propose deep learning networks that generate color images by fusing reflection and range data from LiDAR point clouds. In the proposed networks, the two datasets are fused in three ways—early, mid, and last fusion techniques. The baseline network is the encoder-decoder structured fully convolution network (ED-FCN). The image generation performances were evaluated according to source types, including reflection data-only, range data-only, and fusion of the two datasets. The well-known KITTI evaluation data were used for training and verification. The simulation results showed that the proposed last fusion method yields improvements of 0.53 dB, 0.49 dB, and 0.02 in gray-scale peak signal-to-noise ratio (PSNR), color-scale PSNR, and structural similarity index measure (SSIM), respectively, over the conventional reflection-based ED-FCN. Besides, the last fusion method can be applied to real-time applications with an average processing time of 13.56 ms per frame. The methodology presented in this paper would be a powerful tool for generating data from two or more heterogeneous sources.

Application of Opposition Based Improved Firefly Algorithm in Multi-Level Image Thresholding of Color Images using Between Class Variance

2019 ◽  
Vol 12 ◽  
Author(s):  
Abhay Sharma ◽  
Rekha Chaturvedi ◽  
Umesh Dwivedi ◽  
Sandeep Kumar
Keyword(s):  
Image Segmentation ◽  
Firefly Algorithm ◽  
Color Image ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Image Thresholding ◽  
Opposition Based Learning ◽  
Improved Firefly Algorithm ◽  
Engineering Problems ◽  
Multi Level

Background: Image segmentation is the fundamental step in image processing. Multi-level image segmentation for color image is a very complex and time-consuming process which can be defined as non-deterministic optimization problem. Nature inspired meta-heuristics are best suited to solve such problems. Though several algorithms exist; a modification to suit certain class of engineering problems is always welcome. Objective: This paper provides a modified firefly algorithm and its uses for multilevel thresholding in colored images. Opposition based learning is incorporated in the firefly algorithm to improve convergence rate and robustness. Between class variance method of thresholding is used to formulate the objective function. Method: Numerous benchmark images are tested for evaluating the performance of proposed method. Results: The Experimental results validate the performance of Opposition based improved firefly algorithm (OBIFA) for multi-level image segmentation using peak signal to noise ratio (PSNR) and structured similarity index metric (SSIM)parameter. Conclusion: The OBIFA algorithm is best suited for multilevel image thresholding. It provides best results compared to Darwinian Particle Swarm Optimization (DPSO) and Electro magnetism optimization (EMO) for the parameter: convergence speed, PSNR and SSIM values.

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