Color image segmentation based on improved sine cosine optimization algorithm

Segmentation refers to the process of dividing an image into multiple regions based on some criteria such as intensity and color. In recent years, color image segmentation has received considerable attention from the researchers. However, it is still a highly complicated task due to the presence of...

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Bibliographic Details
Published in:	Soft computing (Berlin, Germany) Vol. 26; no. 23; pp. 13193 - 13203
Main Authors:	Mookiah, Sivasubramanian, Parasuraman, Kumar, Kumar Chandar, S.
Format:	Journal Article
Language:	English
Published:	Berlin/Heidelberg Springer Berlin Heidelberg 01-12-2022
Subjects:	Artificial Intelligence Computational Intelligence Control Engineering Focus Mathematical Logic and Foundations Mechatronics Robotics Metaheuristics algorithm PSNR Color image segmentation Enhanced sine cosine algorithm Particle swarm optimization
Online Access:	Get full text
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Summary:	Segmentation refers to the process of dividing an image into multiple regions based on some criteria such as intensity and color. In recent years, color image segmentation has received considerable attention from the researchers. However, it is still a highly complicated task due to the presence of more attributes or components as compared to monochrome images. Numerous meta-heuristics algorithms are developed to determine the optimal threshold value for segmenting color images efficiently. This paper presents an enhanced sine cosine algorithm (ESCA) to seek threshold for segmenting color images. Sine cosine algorithm (SCA) is a population-based optimization algorithm which has the ability of preventing local minima problem. First an input image is transformed to CIE Lab* color reduced space. ESCA is applied to determine the optimal threshold values for segmentation. The performance of the proposed method is tested on color images from Berkeley database, and segmentation results are compared with two metaheuristic algorithms, namely particle swarm optimization (PSO) and standard SCA. Experimental results are validated by measuring peak signal–noise ratio (PSNR), structural similarity index and computation time for all the images investigated. Results revealed that the proposed method outperforms the other methods like PSO and SCA by achieving PSNR of 23 dB and SSIM of 0.93 and also require less time for finding optimal threshold values than PSO and SCA.
ISSN:	1432-7643 1433-7479
DOI:	10.1007/s00500-022-07133-5