Multilevel thresholding based on Chaotic Darwinian Particle Swarm Optimization for segmentation of satellite images

Suresh, Shilpa; Lal, Shyam

doi:10.1016/j.asoc.2017.02.005

Cited by 89 publications

(21 citation statements)

References 44 publications

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“…The particle swarm optimization algorithm has the characteristics of fast convergence, easy experimentation, and easy combination with other algorithms. It has been widely used in many fields, such as economic dispatch, robot application, signal processing, and image segmentation (Mahor, Prasad, & Rangnekar, 2009;Sengupta & Das, 2017;Suresh & Lal, 2017;Zhang, Gong, & Zhang, 2013). Therefore, the PSO is employed to optimize the parameters during the clarification process of the sugarcane juice.…”

Section: Parameter Optimizationmentioning

confidence: 99%

Data‐driven modeling based on kernel extreme learning machine for sugarcane juice clarification

Meng

Wang

et al. 2019

Food Science & Nutrition

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Clarification of sugarcane juice is an important operation in the production process of sugar industry. The gravity purity and the color value of juice are the two most important evaluation indexes in the cane sugar production using the sulphitation clarification method. However, in the actual operation, the measurement of these two indexes is usually obtained by offline experimental titration, which makes it impossible to timely adjust the system indicators. A data‐driven modeling based on kernel extreme learning machine is proposed to predict the gravity purity of juice and the color value of clear juice. The model parameters are optimized by particle swarm optimization. Experiments are conducted to verify the effectiveness and superiority of the modeling method. Compared with BP neural network, radial basis neural network, and support vector machine, the model has a good performance, which proves the reliability of the model.

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Section: Parameter Optimizationmentioning

confidence: 99%

Data‐driven modeling based on kernel extreme learning machine for sugarcane juice clarification

Meng

Wang

et al. 2019

Food Science & Nutrition

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“…PSNR [33,34] is a pixel difference measurement technique. This is computed by averaging the squared intensity of original image and output image.…”

Section: Segmentation Quality Parametersmentioning

confidence: 99%

StuCoSReC. Proceedings of the 2018 5th Student Computer Science Research Conference.

2018

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“…Both of these algorithms are widely used in optimization problems [5]. From previous research in [6] [7] [8], PSO is more efficient and appropriate if used to find the optimal threshold in multi-level thresholding. However, as in general the PSO algorithm one of the disadvantages is trapped in optimal local and premature convergence in handling complex optimization problems [9].…”

Section: Introductionmentioning

confidence: 99%

Multilevel Thresholding Segmentation based on Otsu’s Method and Autonomous Groups Particle Swarm Optimization for Multispectral Image

Murinto¹,

Prahara²

2019

IJCA

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Segmentation is a process of division of images into certain regions based on certain similarities. Multispectral image consists of several bands with high dimensions, requiring a different method with the problem of low-dimensional images. Multilevel thresholding problems based on Otsu criteria are discussed in this paper. One disadvantage of the Otsu method is that computing time increases exponentially according to the number of thresholding dimensions. In this paper, the Particle Swarm Optimization (PSO) algorithm combined with the Otsu Method called multilevel thresholding Autonomous Groups Particles Swarm Optimization (MAGPSO) is proposed to reduce the two problems of PSO entrapment in the local minima and the slow rate of convergence in solving high dimensional problems. MAGPSO is used for multilevel thresholding image segmentation. The performance of MAGPSO is compared with standard PSO on three natural images. The parameters used to compare the performance of MAGPSO and PSO are the best fitness value, optimal threshold obtained from each algorithm and the measurement of the quality of segmentation results, namely: SSIM, PSNR, and MSE. From the experimental results show that MAGPSO is better when compared to PSO in image segmentation, in terms of the resulting fitness value and higher SSIM and PNSR values.

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Multilevel thresholding based on Chaotic Darwinian Particle Swarm Optimization for segmentation of satellite images

Cited by 89 publications

References 44 publications

Data‐driven modeling based on kernel extreme learning machine for sugarcane juice clarification

Data‐driven modeling based on kernel extreme learning machine for sugarcane juice clarification

StuCoSReC. Proceedings of the 2018 5th Student Computer Science Research Conference.

Multilevel Thresholding Segmentation based on Otsu’s Method and Autonomous Groups Particle Swarm Optimization for Multispectral Image

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