Novel Image Segmentation Using Particle Swarm Optimization

Lokhande, Netra; Pujeri, Ramachandra V.

doi:10.1145/3208955.3208962

Cited by 6 publications

(6 citation statements)

References 4 publications

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“…In [21], the method of segmentation based on the basic algorithm of a swarm of particles is proposed. The method [21] makes it possible to determine the centers of the swarm and the optimal value of the objective function. The disadvantage of [21] is its instability in the early stages of solving the optimization problem.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

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Devising a method for segmenting camouflaged military equipment on images from space surveillance systems using a genetic algorithm

Khudov

Makoveichuk

Butko

et al. 2022

EEJET

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The object of this research is the process of segmentation of camouflaged military equipment in images from space surveillance systems. The method of segmentation of camouflaged military equipment in images from space surveillance systems has been improved using a genetic algorithm. Unlike known methods, the method of segmentation of camouflaged military equipment using a genetic algorithm involves the following: – highlighting brightness channels in the Red-Green-Blue color space; – the use of a genetic algorithm in the image in each channel of brightness of the RGB color space; – image segmentation is reduced to the formation of generations and populations of chromosomes, the calculation of the objective function, selection, crossing, mutation, and decoding of chromosomes in each brightness channel of the Red-Green-Blue color space. Experimental studies were conducted on the segmentation of camouflaged military equipment using a genetic algorithm. It is established that the improved method of segmentation using a genetic algorithm makes it possible to segment images from space surveillance systems. A comparison of the quality of segmentation was carried out. It is established that the improved method of segmentation using a genetic algorithm reduces segmentation errors in the following way: – compared to the known k-means method, by an average of 15 % of errors of the first kind and an average of 7 % of errors of the second kind; – compared to the method of segmentation based on the algorithm of swarm of particles, by an average of 3.8 % of errors of the first kind and an average of 2.9 % of errors of the second kind. The improved segmentation method using a genetic algorithm can be implemented in software and hardware imaging systems from space surveillance systems

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Section: Literature Review and Problem Statementmentioning

confidence: 99%

“…The method [21] makes it possible to determine the centers of the swarm and the optimal value of the objective function. The disadvantage of [21] is its instability in the early stages of solving the optimization problem.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Devising a method for segmenting camouflaged military equipment on images from space surveillance systems using a genetic algorithm

Khudov

Makoveichuk

Butko

et al. 2022

EEJET

View full text Add to dashboard Cite

show abstract

Section: Literature Review and Problem Statementmentioning

confidence: 99%

“…Paper [15] proposes the use of a basic particle swarm algorithm to segment an image. The method from [15] randomly assigns the centers of the swarm, and the best value of the objective function is initialized on the histogram of the image. The disadvantage is a sharp coincidence in the early stages of the search process and, as a result, the impossibility of obtaining significant improvements in this process.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Devising a method for segmenting complex structured images acquired from space observation systems based on the particle swarm algorithm

Khudov

Makoveichuk

Khizhnyak

et al. 2022

EEJET

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This paper considers the improved method for segmenting complex structured images acquired from space observation systems based on the particle swarm algorithm. Unlike known ones, the method for segmenting complex structured images based on the particle swarm algorithm involves the following: – highlighting brightness channels in the Red-Green-Blue color space; – using a particle swarm method in the image in each channel of brightness of the RGB color space; – image segmentation is reduced to calculating the objective function, moving speed, and a new location for each swarm particle in the image in each RGB color space brightness channel. Experimental studies have been conducted on the segmentation of a complex structured image by a method based on the particle swarm algorithm. It was established that the improved segmentation method based on the particle swarm algorithm makes it possible to segment complex structured images acquired from space surveillance systems. A comparison of the quality of segmenting a complex structured image was carried out. The comparative visual analysis of well-known and improved segmentation methods indicates the following: – the improved segmentation method based on the particle swarm algorithm highlights more objects of interest (objects of military equipment); – the well-known k-means method assigns some objects of interest (especially those partially covered with snow) to the snow cover (marked in blue); – the improved segmentation method also associates some objects of interest that are almost completely covered with snow with the snow cover (marked in blue). It has been established that the improved segmentation method based on the particle swarm algorithm reduces segmentation errors of the first kind by an average of 12 % and reduces segmentation errors of the second kind by an average of 8 %

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“…Meta-heuristics are widely used in various scientific and engineering applications because they have shown good performance in solving large-scale, complex non-linear and non-differentiable problems. The applications range from data mining [20], image processing [21] and social network analysis [22] in computer science domain, at one end of the spectrum, to air traffic control [23], airfoil design [55], optical buffer design [53] in industrial field, at the other side of spectrum. However, according to the famous "No Free Lunch" theory [24], there is no meta-heuristic best suited for solving all optimization problems.…”

Section: H Emamimentioning

confidence: 99%

Chaotic Election Algorithm

Emami¹

2019

cai

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A novel Chaotic Election Algorithm (CEA) is presented for numerical function optimization. CEA is a powerful enhancement of election algorithm. The election algorithm is a socio-politically inspired strategy that mimics the behavior of candidates and voters in presidential election process. In election algorithm, individuals are organized as electoral parties. Advertising campaign forms the basis of the algorithm in which individuals interact or compete with one other using three operators: positive advertisement, negative advertisement and coalition. Advertising campaign hopefully causes the individuals converge to the global optimum point in solution space. However, election algorithm suffers from a fundamental challenge: it gets stuck at local optima due to the inability of advertising campaign in searching solution space. CEA enhances the election algorithm through modifying party formation step, introducing chaotic positive advertisement and migration operator. By chaotic positive advertisement, CEA exploits the entire solution space, what increases the probability of obtaining global optimum point. By migration, CEA increases the diversity of the population and prevents early convergence of the individuals. The proposed CEA algorithm is tested on 28 well-known standard boundary-constrained test functions, and the results are verified by a comparative study with several well-known meta-heuristics. The results demonstrate that CEA is able to provide significant improvement over canonical election algorithm and other comparable algorithms.

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Novel Image Segmentation Using Particle Swarm Optimization

Cited by 6 publications

References 4 publications

Devising a method for segmenting camouflaged military equipment on images from space surveillance systems using a genetic algorithm

Devising a method for segmenting camouflaged military equipment on images from space surveillance systems using a genetic algorithm

Devising a method for segmenting complex structured images acquired from space observation systems based on the particle swarm algorithm

Chaotic Election Algorithm

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