Color image segmentation based on improved sine cosine optimization algorithm

Title

Color image segmentation based on improved sine cosine optimization algorithm

Creator

Mookiah S.; Parasuraman K.; Kumar Chandar S.

Description

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 L*a*b* 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 signalnoise 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 23dB and SSIM of 0.93 and also require less time for finding optimal threshold values than PSO and SCA. 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

Source

Soft Computing, Vol-26, No. 23, pp. 13193-13203.

Date

2022-01-01

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

Color image segmentation; Enhanced sine cosine algorithm; Metaheuristics algorithm; Particle swarm optimization; PSNR

Coverage

Mookiah S., Department of Computer Science, JP College of Arts and Science, Tenkasi, India; Parasuraman K., Centre for Information Technology and Engineering, Manonmaniam Sundaranar University, Tamil Nadu, Tirunelveli, 627012, India; Kumar Chandar S., School of Business and Management CHRIST (Deemed to be University), Karnataka, Bangalore, 560029, India

Rights

Restricted Access

Relation

ISSN: 14327643

Format

Online

Language

English

Type

Article

Identifier

https://doi.org/10.1007/s00500-022-07133-5

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85132966366&doi=10.1007%2fs00500-022-07133-5&partnerID=40&md5=0d448a6c59fc78e567c7ef217d853743