Chimp optimization algorithm in multilevel image thresholding and image clustering Chimp optimization algorithm in multilevel image thresholding and image clustering

Multilevel image thresholding and image clustering, two extensively used image processing techniques, have sparked renewed interest in recent years due to their wide range of applications. The approach of yielding multiple threshold values for each color channel to generate clustered and segmented i...

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Vydáno v:Evolving systems Ročník 14; číslo 4; s. 605 - 648
Hlavní autoři: Eisham, Zubayer Kabir, Haque, Md. Monzurul, Rahman, Md. Samiur, Nishat, Mirza Muntasir, Faisal, Fahim, Islam, Mohammad Rakibul
Médium: Journal Article
Jazyk:angličtina
Vydáno: Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2023
Springer Nature B.V
Témata:
Algorithms
Artificial Intelligence
Clustering
Color
Complex Systems
Complexity
Engineering
Heuristic methods
Image processing
Image quality
Image segmentation
Maximum entropy method
Multilevel
Optimization algorithms
Original Paper
Particle swarm optimization
Performance evaluation
Performance measurement
Popularity
Signal to noise ratio
Similarity
Wavelet transforms
Optimization algorithm
Metaheuristic
Clustering
Thresholding
ChOA
ISSN:1868-6478, 1868-6486, 1868-6486
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Shrnutí:Multilevel image thresholding and image clustering, two extensively used image processing techniques, have sparked renewed interest in recent years due to their wide range of applications. The approach of yielding multiple threshold values for each color channel to generate clustered and segmented images appears to be quite efficient and it provides significant performance, although this method is computationally heavy. To ease this complicated process, nature inspired optimization algorithms are quite handy tools. In this paper, the performance of Chimp Optimization Algorithm (ChOA) in image clustering and segmentation has been analyzed, based on multilevel thresholding for each color channel. To evaluate the performance of ChOA in this regard, several performance metrics have been used, namely, Segment evolution function, peak signal-to-noise ratio, Variation of information, Probability Rand Index, global consistency error, Feature Similarity Index and Structural Similarity Index, Blind/Referenceless Image Spatial Quality Evaluatoe, Perception based Image Quality Evaluator, Naturalness Image Quality Evaluator. This performance has been compared with eight other well known metaheuristic algorithms: Particle Swarm Optimization Algorithm, Whale Optimization Algorithm, Salp Swarm Algorithm, Harris Hawks Optimization Algorithm, Moth Flame Optimization Algorithm, Grey Wolf Optimization Algorithm, Archimedes Optimization Algorithm, African Vulture Optimization Algorithm using two popular thresholding techniques-Kapur’s entropy method and Otsu’s class variance method. The results demonstrate the effectiveness and competitive performance of Chimp Optimization Algorithm.
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ISSN:1868-6478
1868-6486
1868-6486
DOI:10.1007/s12530-022-09443-3