A comparative study of various meta-heuristic techniques applied to the multilevel thresholding problem

Hammouche, Kamal; Diaf, Moussa; Siarry, Patrick

doi:10.1016/j.engappai.2009.09.011

Cited by 195 publications

(70 citation statements)

References 30 publications

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“…A study by Hammouche et al [4] presented an important survey of how different evolutionary methods were used to solve the problems of Otsu and Kapur. One of the methods included in the evolutionary method is the Genetic Algorithm (GA) [5].…”

Section: Introductionmentioning

confidence: 99%

Evolutionary Optimization Methods for Animal Leather Image Segmentation

Murinto¹,

Winiarti²,

Prahara³

et al. 2018

IJCA

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One of the processes undertaken to determine the quality of animal skin for leather craft in the form of digital images is the process of image segmentation. A disability detection process is required to ensure that animal skins used for craft are right-near as expected. In this research, the evolutionary method is used to process Darwinian segmentation of particle swarm optimization (DPSO) methods used for animal image segmentation process. The results were then compared with the Harmonic Search Algorithm (HSA) method. The process of image segmentation is important, if segmentation is done correctly then the next process will run correctly too. One of the problems usually faced is how to choose a good image segmentation method. Experiments had been done on four categories of animal leather images namely lizard leather, sheep leather, cow leather and goat leather. PSNR values were used to measure the quality of the segmentation method used. From the experimental results it is seen that there is little difference between the DPSO and HSA methods used in this study. It can be concluded, however, that the DPSO method is slightly better than the HSA in certain animal cases. PSNR value is higher when compared to using HSA methods on the same image.

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Section: Introductionmentioning

confidence: 99%

Evolutionary Optimization Methods for Animal Leather Image Segmentation

Murinto¹,

Winiarti²,

Prahara³

et al. 2018

IJCA

View full text Add to dashboard Cite

show abstract

“…On the other hand, when pixels are separated into more than two classes, the task is named as multilevel thresholding (MT) and demands more than one th values [2,10]. Threshold based methods are divided into parametric and nonparametric [2][3][4]. For parametric approaches it is necessary to estimate some parameters of a probability density function which models each class.…”

Section: Introductionmentioning

confidence: 99%

“…Hammouche et al provides a survey of different evolutionary algorithms such as (Differential Evolution (DE), Simulated Annealing (SA), Tabu Search (TS) etc. ), used to solve the Kaptur's and Otsu's problems [2]. In [2,11,12], Genetic Algorithms-based approaches are employed to segment multi-classes.…”

Section: Introductionmentioning

confidence: 99%

“…), used to solve the Kaptur's and Otsu's problems [2]. In [2,11,12], Genetic Algorithms-based approaches are employed to segment multi-classes. Similarly in [1,5], Particle Swarm Optimization (PSO) [13] has been proposed for MT proposes, maximizing the Otsu's function.…”

Section: Introductionmentioning

confidence: 99%

“…When the image is segmented into two classes, the task is called bi-level thresholding (BT) and it requires only one th value. On the other hand, when pixels are separated into more than two classes, the task is named as multilevel thresholding (MT) and demands more than one th values [2,10]. Threshold based methods are divided into parametric and nonparametric [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Multilevel Thresholding algorithm using electromagnetism optimization

et al. 2014

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Segmentation is one of the most important tasks in image processing. It consist in classify the pixels into two or more groups depending on their intensity levels and a threshold value. The quality of the segmentation depends on the method applied to select the threshold. The use of the classical implementations for multilevel thresholding is computationally expensive since they exhaustively search the best values to optimize the objective function. Under such conditions, the use of optimization evolutionary approaches has been extended. The Electro-magnetism-Like algorithm (EMO) is an evolutionary method which mimics the attraction-repulsion mechanism among charges to evolve the members of a population. Different to other algorithms, EMO exhibits interesting search capabilities whereas maintains a low computational overhead. In this paper, a multilevel thresholding (MT) algorithm based on the EMO is introduced. The approach combines the good search capabilities of EMO algorithm with objective functions proposed by the popular MT methods of Otsu and Kapur. The algorithm takes random samples from a feasible search space inside the image histogram. Such samples build each particle in the EMO context whereas its quality is evaluated considering the objective that is function employed by the Otsu's or Kapur's method. Guided by these objective values the set of candidate solutions are evolved through the EMO operators until an optimal solution is found. The approach generates a multilevel segmentation algorithm which can effectively identify the threshold values of a digital image in a reduced number of iterations. Experimental results show performance evidence of the implementation of EMO for digital image segmentation.

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2019

Quantum Inspired Meta‐heuristics for Image Analysis

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A comparative study of various meta-heuristic techniques applied to the multilevel thresholding problem

Cited by 195 publications

References 30 publications

Evolutionary Optimization Methods for Animal Leather Image Segmentation

Evolutionary Optimization Methods for Animal Leather Image Segmentation

A Multilevel Thresholding algorithm using electromagnetism optimization

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