Multi-level image thresholding using Otsu and chaotic bat algorithm

Multi-level thresholding is a helpful tool for several image segmentation applications. Evaluating the optimal thresholds can be applied using a widely adopted extensive scheme called Otsu’s thresholding. In the current work, bi-level and multi-level threshold procedures are proposed based on their...

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Veröffentlicht in:Neural computing & applications Jg. 29; H. 12; S. 1285 - 1307
Hauptverfasser: Satapathy, Suresh Chandra, Sri Madhava Raja, N., Rajinikanth, V., Ashour, Amira S., Dey, Nilanjan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Springer London 01.06.2018
Springer Nature B.V
Schlagworte:
Algorithms
Artificial Intelligence
Automatic target recognition
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Data Mining and Knowledge Discovery
Heuristic methods
Image processing
Image Processing and Computer Vision
Image segmentation
Object recognition
Original Article
Particle swarm optimization
Probability and Statistics in Computer Science
Signal to noise ratio
Target recognition
Thresholds
Multi-level thresholding
Bat algorithm
Structural similarity index (SSIM)
Otsu method
Ikeda Map
Peak signal to noise ratio (PSNR)
ISSN:0941-0643, 1433-3058
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Zusammenfassung:Multi-level thresholding is a helpful tool for several image segmentation applications. Evaluating the optimal thresholds can be applied using a widely adopted extensive scheme called Otsu’s thresholding. In the current work, bi-level and multi-level threshold procedures are proposed based on their histogram using Otsu’s between-class variance and a novel chaotic bat algorithm (CBA). Maximization of between-class variance function in Otsu technique is used as the objective function to obtain the optimum thresholds for the considered grayscale images. The proposed procedure is applied on a standard test images set of sizes (512 × 512) and (481 × 321). Further, the proposed approach performance is compared with heuristic procedures, such as particle swarm optimization, bacterial foraging optimization, firefly algorithm and bat algorithm. The evaluation assessment between the proposed and existing algorithms is conceded using evaluation metrics, namely root-mean-square error, peak signal to noise ratio, structural similarity index, objective function, and CPU time/iteration number of the optimization-based search. The results established that the proposed CBA provided better outcome for maximum number cases compared to its alternatives. Therefore, it can be applied in complex image processing such as automatic target recognition.
Bibliographie:ObjectType-Article-1
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ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-016-2645-5