An experimentation of objective functions used for multilevel thresholding based image segmentation using particle swarm optimization

For image segmentation, multilevel thresholding is treated as one of the widely used approach. However, this approach has a major issue that is it suffers of high computational complexity problem with the increase threshold levels. This paper presented a comparative performance analysis of some obje...

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Veröffentlicht in:International journal of information technology (Singapore. Online) Jg. 16; H. 3; S. 1717 - 1732
Hauptverfasser: Ahmed, Saifuddin, Biswas, Anupam, Khairuzzaman, Abdul Kayom Md
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Singapore Springer Nature Singapore 01.03.2024
Springer Nature B.V
Schlagworte:
Algorithms
Artificial Intelligence
Computer Imaging
Computer Science
Datasets
Entropy
Experimentation
Heuristic
Image Processing and Computer Vision
Image quality
Image segmentation
Machine Learning
Mean
Multilevel
Optimization techniques
Original Research
Particle swarm optimization
Pattern Recognition and Graphics
Random variables
Signal to noise ratio
Software Engineering
Surveillance
Tomography
Vision
Kapur’s entropy
Multilevel thresholding
Non-parametric
Masi entropy
Parametric
Meta-heuristic
Particle swarm optimization
Otsu’s method
ISSN:2511-2104, 2511-2112
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Zusammenfassung:For image segmentation, multilevel thresholding is treated as one of the widely used approach. However, this approach has a major issue that is it suffers of high computational complexity problem with the increase threshold levels. This paper presented a comparative performance analysis of some objective functions used for image segmentation using the concept of multilevel thresholding and a modified version of adaptive inertia weight Particle Swarm Optimization (PSO) technique. The PSO algorithm is applied to multilevel thresholding based image segmentation using either Otsu’s inter class variance, Kapur’s entropy or Masi entropy as an objective function. Each method is tested over various standard image dataset like Berkeley image database, USC-SIPI image dataset etc. The evaluated result of each method has been compared and the overall experimentation is performed in three different ways such as, PSO and Otsu’s method, PSO and Kapur’s entropy and PSO with Masi entropy. The performance and quality of the segmented images are measured using the following parameters such as, average Mean Structural Similarity Index (MSSIM), average Peak Signal to Noise Ratio (PSNR) values, average mean objective functions values and average CPU rum time values. The experimental analysis shows that the performance of Otsu’s inter-class variance function shows comparatively a better result than Kapur’s and Masi’s entropic method.
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ISSN:2511-2104
2511-2112
DOI:10.1007/s41870-023-01606-y