Abstract
The image segmentation techniques based on multi-level threshold value received lot of attention in recent years. It is because they can be used as a pre-processing step in complex image processing applications. The main problem in identifying the suitable threshold values occurs when classical image segmentation methods are employed. The swarm intelligence (SI) technique is used to improve multi-level threshold image (MTI) segmentation performance. SI technique simulates the social behaviors of swarm ecosystem, such as the behavior exhibited by different birds, animals etc. Based on SI techniques, we developed an alternative MTI segmentation method by using a modified version of the salp swarm algorithm (SSA). The modified algorithm improves the performance of various operators of the moth-flame optimization (MFO) algorithm to address the limitations of traditional SSA algorithm. This results in improved performance of SSA algorithm. In addition, the fuzzy entropy is used as objective function to determine the quality of the solutions. To evaluate the performance of the proposed methodology, we evaluated our techniques on CEC2005 benchmark and Berkeley dataset. Our evaluation results demonstrate that SSAMFO outperforms traditional SSA and MFO algorithms, in terms of PSNR, SSIM and fitness value.
Highlights
In recent years, swarm intelligence (SI) methods received wide attention since they are applied in different areas and applications of Economics, Chemistry, and Medicine [1]
We present an alternative image segmentation model based on improving the performance of the salp swarm algorithm (SSA) using the moth flame optimization (MFO) algorithm
From the results given in this table, it can be noticed that the SSAMFO allocates the first rank with the highest PSNR value at 32 cases
Summary
SI methods received wide attention since they are applied in different areas and applications of Economics, Chemistry, and Medicine [1]. The SI approaches are applied in different image processing fields, such as computer vision, face recognition, object identification, etc. Image segmentation can be used in pre-processing stage of various applications, such as medical diagnosis [2] and satellite image processing [3]. Image segmentation is used to split an image into different classes with similar properties (such as texture, contrast, gray level, brightness, and color) and are based on a predefined criterion. There are several approaches have been applied for image segmentation, including edge detection [4], The associate editor coordinating the review of this manuscript and approving it for publication was Jihwan P.
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