Multilevel thresholding hyperspectral image segmentation based on independent component analysis and swarm optimization methods

Murinto, Murinto; Astuti, Nur Rochmah Dyah Puji; Mardhia, Murein Miksa

doi:10.26555/ijain.v5i1.311

Cited by 7 publications

(4 citation statements)

References 20 publications

(27 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…First, the peak signal to noise ratio (PSNR) quality index is used to measure the similarity of the image segmentation results to the reference image (image before segmentation) which based on the results of the mean square error (MSE), where MSE is calculated from the average intensity of the square of the original image (input) and the resulting image pixel (output). MSE and PSNR values are defined in equations (13) and equations (14).…”

Section: Experiments Results and Analysismentioning

confidence: 99%

See 1 more Smart Citation

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

Murinto¹,

Prahara²

2019

IJCA

View full text Add to dashboard Cite

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.

show abstract

Section: Experiments Results and Analysismentioning

confidence: 99%

“…The size of the SNR is an estimate of the quality of the segmented image compared to the original image. PSNR is defined as equation (14).…”

Section: Experiments Results and Analysismentioning

confidence: 99%

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

Murinto¹,

Prahara²

2019

IJCA

View full text Add to dashboard Cite

show abstract

“…In [61], the authors proposed a combination of the multi-level threshold segmentation and three PSO versions (classic PSO, Darwinian PSO (DPSO), and fractional-order PSO (FODPSO)). DPSO consisted of removing and/or creating particles based on the associated worst/best fitness value using fractional calculus.…”

Section: Image Segmentation Based On Genetic Algorithms (Ga)mentioning

confidence: 99%

Emerging Applications of Bio-Inspired Algorithms in Image Segmentation

2021

View full text Add to dashboard Cite

Image processing is one example of digital media. It consists of a set of operations to handle an image. Image segmentation is among its main important operations. It involves dividing the image into several parts or regions to extract vital information or identify relevant objects. Many techniques of artificial intelligence, including bio-inspired algorithms, have been used in this regard. This article collected the state-of-the-art studies presenting image-segmentation techniques combined with four bio-inspired algorithms including particle swarm optimization (PSO), genetic algorithms (GA), ant colony optimization (ACO), and artificial bee colonies (ABC). This research work aimed at showing the importance of image segmentation and its combination with these algorithms. This article provides insights on how these algorithms are adapted to image-segmentation combinatorial problems, which assist researchers to start the first hands-on application. It also discusses their setting parameters and the highly used algorithms such as PSO, GA, ACO, and ABC. The article presents new research directions in image segmentation based on bio-inspired algorithms.

show abstract

“…This mainly caused by its easy implementation and less memory consumption (Sathya & Kayalvizhi, 2010). Several studies of image segmentation using multilevel thresholding were carried out by (Rochmah et al, 2019;Dhieb & Frikha, 2016;Pare et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Multilevel Thresholding Image Segmentation Based-Logarithm Decreasing Inertia Weight Particle Swarm Optimization

Prahara¹,

Ujianto²

2022

ijasca

View full text Add to dashboard Cite

The image segmentatation technique that is often used is thresholding. Image segmentation is a process of dividing the image into different regions according to their similar characteristics. This research proposes a multilevel thresholding algorithm using modified particle swarm optimization to solve a segmentation problem. The threshold optimal values are determined by maximizing Otsu’s objective function using optimization technique namely particle swarm optimization based on the logarithmic decreasing inertia weight (LogDIWPSO). The proposed method reduces the computational time to find the optimum thresholds of multilevel thresholding which evaluated on several grayscale images. A detailed comparison analysis with other multilevel thresholding based techniques namely particle swarm optimization (PSO), iterative particle swarm optimization (IPSO), and genetic algorithms (GA), From the experiments, Modified particle swarm optimization (MoPSO) produces better performance compared to the other methods in terms of fitness value, robustness and convergence. Therefore, it can be concluded that MoPSO is a good approach in finding the optimal threshold value. Keywords: grayscale image, inertia weight, image segmentation, particle swarm optimization.

show abstract

Multilevel thresholding hyperspectral image segmentation based on independent component analysis and swarm optimization methods

Cited by 7 publications

References 20 publications

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

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

Emerging Applications of Bio-Inspired Algorithms in Image Segmentation

Multilevel Thresholding Image Segmentation Based-Logarithm Decreasing Inertia Weight Particle Swarm Optimization

Contact Info

Product

Resources

About