A hybrid firefly and particle swarm optimization algorithm applied to multilevel image thresholding
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A hybrid firefly and particle swarm optimization algorithm applied to multilevel image thresholding Taymaz Rahkar Farshi1 · Ahad K. Ardabili2 Received: 1 October 2020 / Accepted: 29 October 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract There are many techniques for conducting image analysis and pattern recognition. This papers explores a way to optimize one of these techniques—image segmentation—with the help of a novel hybrid optimization algorithm. Image segmentation is mostly used for a semantic segmentation of images, and thresholding is one the most common techniques for performing this segmentation. Otsu’s and Kapur’s thresholding methods are two well-known approaches, both of which maximize the between-class variance and the entropy measure, respectively, in a gray image histogram. Both techniques were developed for bi-level thresholding. However, these techniques can be extended to multilevel image thresholding. For this to occur, a large number of iterations are required to account for exact threshold values. However, various optimization techniques have been used to overcome this drawback. In this study, a hybrid firefly and particle swarm optimization algorithm has been applied to yield optimum threshold values in multilevel image thresholding. The proposed method has been assessed by comparing it with four well-known optimization algorithms. The comprehensive experiments reveal that the proposed method achieves better results in term of fitness value, PSNR, SSIM, FSIM, and SD. Keywords Image segmentation · Multilevel thresholding · Kapur’s function · Otsu’s function · Hybrid optimization
1 Introduction There are many ways to conduct an image analysis or run pattern recognition. The use of image segmentation is growing, particularly because digital cameras are becoming more accessible for implementations. The process of image segmentation divides an image into homogenous regions and classifies the pixels. Some of the practical applications of image segmentation are satellite images [1], agriculture automation [2], medical images [3, 4], traffic control systems [5] and optical character recognition (OCR) [6]. An RGB image consists of three primary color channels [7]: red (R), green (G), and blue (B). In most cases RGB images are converted to grayscale because intensity can still Communicated by Y. Zhang. * Ahad K. Ardabili [email protected] 1
Software Engineering Department, Ayvansaray University, 34020 Istanbul, Turkey
Department of Basic Sciences, Altınbaş University, 34217 Istanbul, Turkey
2
be measured, and it is considered sufficient to showcase the possible performance gain in image segmentation. There is a wide variety of image segmentation techniques, and thresholding methods based on image histograms are some of the most widely used segmentation methods. These histogram-based methods are very fast and effective compared to other existing segmentation methods because they only need to pass through the pixel once [8, 9]. Moreover,
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