Spatial context cross entropy function based multilevel image segmentation using multi-verse optimizer

In this paper, a context-sensitive energy curve based cross-entropy method for multilevel color image segmentation is proposed. In thresholding approaches, pixels are arranged in various regions based on their intensity level. The main challenge generally faced in multilevel thresholding is the sele...

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Bibliographic Details
Published in:Multimedia tools and applications 2019-08, Vol.78 (16), p.22613-22641
Main Authors: Kandhway, Pankaj, Bhandari, Ashish Kumar
Format: Article
Language:English
Subjects:
Algorithms
Computational efficiency
Computer Communication Networks
Computer Science
Data Structures and Information Theory
Entropy
Entropy (Information theory)
Heuristic methods
Image segmentation
Multilevel
Multimedia Information Systems
Optimization
Optimization techniques
Pixels
Special Purpose and Application-Based Systems
Thresholds
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Summary:In this paper, a context-sensitive energy curve based cross-entropy method for multilevel color image segmentation is proposed. In thresholding approaches, pixels are arranged in various regions based on their intensity level. The main challenge generally faced in multilevel thresholding is the selection of best threshold values for the pixel division. However, the combination of the energy curve and the minimum cross entropy (Energy-MCE) scheme provides appropriate thresholds for a multilevel approach, but the computational cost for selecting optimal thresholds is high. Therefore, the selection of meta-heuristic optimization algorithms reduces this cost and generates optimal thresholds. A multi-verse optimizer (MVO) algorithm based on Energy-MCE thresholding approach is proposed to search the accurate and near-optimal thresholds for segmentation. Tests on natural images showed that the proposed method achieves better performance than the well-known optimization techniques in many challenging cases or images, such as identifying weak objects and revealing fine structures of complex objects while the added computational cost is minimal.
ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-019-7506-7