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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| Vydané v: | Soft computing (Berlin, Germany) Ročník 26; číslo 23; s. 13193 - 13203 |
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| Jazyk: | English |
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Berlin/Heidelberg
Springer Berlin Heidelberg
01.12.2022
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| ISSN: | 1432-7643, 1433-7479 |
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| Abstract | 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 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. |
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| AbstractList | 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 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. |
| Author | Mookiah, Sivasubramanian Kumar Chandar, S. Parasuraman, Kumar |
| Author_xml | – sequence: 1 givenname: Sivasubramanian surname: Mookiah fullname: Mookiah, Sivasubramanian email: sivasu4all@gmail.com organization: Department of Computer Science, JP College of Arts and Science – sequence: 2 givenname: Kumar surname: Parasuraman fullname: Parasuraman, Kumar organization: Centre for Information Technology and Engineering, Manonmaniam Sundaranar University – sequence: 3 givenname: S. surname: Kumar Chandar fullname: Kumar Chandar, S. organization: School of Business and Management CHRIST (Deemed to be University) |
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| Cites_doi | 10.36548/jscp.2021.1.004 10.1016/j.knosys.2015.12.022 10.1016/j.patrec.2021.01.005 10.1007/s11633-016-0975-5 10.1155/2015/120495 10.1007/s11771-012-1107-1 10.1016/j.tcs.2013.08.005 10.1016/j.asoc.2012.03.072 10.36548/jiip.2021.1.003 10.1007/s00500-015-1677-6 10.1016/j.patcog.2019.03.011 10.1016/j.patcog.2010.07.013 10.1016/j.swevo.2013.02.001 10.1109/ACCESS.2019.289667 10.1016/j.dsp.2016.01.010 10.1016/j.eswa.2016.03.032 10.1007/978-3-642-37192-9_32 10.1155/2014/690349 10.1145/2464576.2466810 10.1007/978-981-10-5565-2_17 10.1007/978-3-642-41181-6_16 10.1109/IMCEC.2016.7867535 10.1109/ICIIP.2011.6108965 |
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| Keywords | Metaheuristics algorithm PSNR Color image segmentation Enhanced sine cosine algorithm Particle swarm optimization |
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