A multilevel thresholding algorithm using HDAFA for image segmentation

Singh, Simrandeep; Mittal, Nitin; Singh, Harbinder

doi:10.1007/s00500-021-05956-2

Cited by 14 publications

(6 citation statements)

References 36 publications

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“…With the development of machine learning theory and methods, many optimization algorithms have emerged and been successfully applied (e.g. Singh et al., 2020 , 2021 ; Mittal et al., 2021 ). Among these optimization algorithms, genetic algorithm (GA) is very suitable for solving nonlinear problems according to the characteristics of population searching strategy, information interchange and searching independent on gradient information, and it has been applied to many fields such as machine learning, function optimization, pattern recognition and so on (e.g.…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis for cooling dehumidifier based on fuzzy classifier optimized by adaptive genetic algorithm

Gao

Wang

2022

Heliyon

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Section: Introductionmentioning

confidence: 99%

Fault diagnosis for cooling dehumidifier based on fuzzy classifier optimized by adaptive genetic algorithm

Gao

Wang

2022

Heliyon

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“…Segmentation aims to extract features and track information of an image. Image segmentation divides the image into a set of non-overlapping contours based on certain properties like texture, color, homogeneity, and structure [37,39]. An automatic image segmentation process always remains a very complex procedure in image processing.…”

Section: Introductionmentioning

confidence: 99%

“…[2,21,26] are defined to calculate optimal threshold values. Some recently proposed MA are the Hybrid Dragonfly algorithm (DA) and Firefly Algorithm (FA) (HDAFA) [39], the Starling Murmuration Optimizer [48], the Conscious Neighborhood-based Crow Search Algorithm (CCSA) [46], the Quantum-based avian navigation optimizer algorithm (QANA) [47], the Oppositionbased Moth Swarm Algorithm (OBMSA) [32], the Archimedes optimization algorithm (AOA) [14], to mention some.…”

Section: Introductionmentioning

confidence: 99%

Improving the segmentation of digital images by using a modified Otsu’s between-class variance

Singh

Mittal

Singh

et al. 2023

Multimed Tools Appl

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Image segmentation is a critical stage in the analysis and pre-processing of images. It comprises dividing the pixels according to threshold values into several segments depending on their intensity levels. Selecting the best threshold values is the most challenging task in segmentation. Because of their simplicity, resilience, reduced convergence time, and accuracy, standard multi-level thresholding (MT) approaches are more effective than bi-level thresholding methods. With increasing thresholds, computer complexity grows exponentially. A considerable number of metaheuristics were used to optimize these problems. One of the best image segmentation methods is Otsu's between-class variance. It maximizes the between-class variance to determine image threshold values. In this manuscript, a new modified Otsu function is proposed that hybridizes the concept of Otsu's between class variance and Kapur's entropy. For Kapur's entropy, a threshold value of an image is selected by maximizing the entropy of the object and background pixels. The proposed modified Otsu technique combines the ability to find an optimal threshold that maximizes the overall entropy from Kapur's and the maximum variance Multimedia Tools and Applications

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“…Compared with particle swarm optimization (PSO), moth flame optimization (MFO), DE, cuckoo search (CS) and grey wolf optimizer (GWO), in the comparative experiment of parameters such as PSNR, SSIM and FSIM, this algorithm is superior in thresholding quality and consistency. Singh et al [13] used Otsu and Kapur entropy to combine dragonfly algorithm (DA) and firefly algorithm (FA). By segmenting the benchmark image of Berkeley segmentation dataset (BSD 500), it shows better results in convergence iteration times, threshold quality and segmentation effect when compared with NIMA such as EMO, GA, PSO and BFO.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Hybrid Coyote Optimization Algorithm for Image Thresholding

Li¹,

Huang²,

Qian³

et al. 2022

Computers, Materials &Amp; Continua

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In order to address the problems of Coyote Optimization Algorithm in image thresholding, such as easily falling into local optimum, and slow convergence speed, a Fuzzy Hybrid Coyote Optimization Algorithm (hereinafter referred to as FHCOA) based on chaotic initialization and reverse learning strategy is proposed, and its effect on image thresholding is verified. Through chaotic initialization, the random number initialization mode in the standard coyote optimization algorithm (COA) is replaced by chaotic sequence. Such sequence is nonlinear and long-term unpredictable, these characteristics can effectively improve the diversity of the population in the optimization algorithm. Therefore, in this paper we first perform chaotic initialization, using chaotic sequence to replace random number initialization in standard COA. By combining the lens imaging reverse learning strategy and the optimal worst reverse learning strategy, a hybrid reverse learning strategy is then formed. In the process of algorithm traversal, the best coyote and the worst coyote in the pack are selected for reverse learning operation respectively, which prevents the algorithm falling into local optimum to a certain extent and also solves the problem of premature convergence. Based on the above improvements, the coyote optimization algorithm has better global convergence and computational robustness. The simulation results show that the algorithm has better thresholding effect than the five commonly used optimization algorithms in image thresholding when multiple images are selected and different threshold numbers are set.

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A multilevel thresholding algorithm using HDAFA for image segmentation

Cited by 14 publications

References 36 publications

Fault diagnosis for cooling dehumidifier based on fuzzy classifier optimized by adaptive genetic algorithm

Fault diagnosis for cooling dehumidifier based on fuzzy classifier optimized by adaptive genetic algorithm

Improving the segmentation of digital images by using a modified Otsu’s between-class variance

Fuzzy Hybrid Coyote Optimization Algorithm for Image Thresholding

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