Hybridization of Levy Flight and Chaotic Gravitational Search Algorithm for Image Segmentation

Image segmentation is an important step in image processing due to its enormous application potential in medical image analysis, data mining, and pattern recognition. In fact, image segmentation is a normalization procedure in which image is divided into individual pixels based on threshold values....

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Bibliographic Details
Published in2022 IEEE International Symposium on Smart Electronic Systems (iSES) pp. 219 - 224
Main Authors Rather, Sajad Ahmad, Das, Sujit
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2022
Subjects
Chaos Theory
Computational efficiency
Computational modeling
Entropy
Gravitational Search Algorithm
Histograms
Image analysis
Image segmentation
Kapur's Entropy
LCGSA
Levy Flight
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Summary:Image segmentation is an important step in image processing due to its enormous application potential in medical image analysis, data mining, and pattern recognition. In fact, image segmentation is a normalization procedure in which image is divided into individual pixels based on threshold values. In this work, Levy flight and Chaos theory based Gravitational Search Algorithm (LCGSA) has been employed for the image segmentation task. In LCGSA, the exploration is carried out by levy flight while exploitation is guaranteed by chaotic maps. Besides, Kapur's entropy method has been utilized to segment the sample image into various regions based on the pixel intensity values. To investigate the segmentation performance of LCGSA, we have used Couple and Pentagon images from the USC-SIPI database. The experimental and simulation analysis has been carried out thoroughly using convergence curves, segmented graphs, box plots, and histogram maps. The overall analysis of the experimental results indicated the efficient performance of LCGSA in terms of providing optimal values for the image thresholds and taking less computational overhead.
DOI:10.1109/iSES54909.2022.00052