A non-revisiting quantum-behaved particle swarm optimization based multilevel thresholding for image segmentation

Yang, Zhenlun; Wu, A.

doi:10.1007/s00521-019-04210-z

Cited by 31 publications

(13 citation statements)

References 57 publications

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“…The performance of this algorithm is compared with seven other algorithms [ 66 ] 2016 CS This paper introduced the comparative performance study of different objective functions using cuckoo search and other optimization algorithms to solve the color image segmentation problem using Otsu or Kapur’s method [ 67 ] 2018 ABC This method presented an Otsu segmentation method based on the ABC algorithm [ 68 ] 2020 PSO This technique was used to segment the color images [ 32 ] 2019 WOA–GWO–PSO This method used three meta-heuristics algorithms for multilevel thresholding image segmentation to maximize the Otsu method. It tested on 20 benchmark test images using six different thresholds [ 69 ] 2018 Firefly algorithm (FA) This is a technique for multilevel color image thresholding used the fuzzy entropy as a fitness function and enhanced the FA algorithm by Levy flight (LF) strategy [ 70 ] 2020 PSO This paper proposed a non-revisiting quantum-behaved PSO (NrQPSO) algorithm to find the optimal multilevel thresholds for gray-level images using Kapur’s entropy as an objective function [ 71 ] 2020 Teaching learning based optimization algorithm (TLBO) In this paper, LebTLBO was applied on ten standard test images and used the Otsu and Kapur’s entropy objective functions for image segmentation and compared with the MTEMO, GA, PSO, and BF algorithms for both Otsu and Kapur’s entropy methods. The results demonstrated that the LebTLBO outperforms the compared algorithms [ 72 ] 2020 DE This paper proposed a beta differential evolution (BDE)-based fast color image multilevel thresholding method using two objective functions (Kapur’s and Tsallis entropy).…”

Section: Literature Reviewmentioning

confidence: 99%

Improved manta ray foraging optimization for multi-level thresholding using COVID-19 CT images

Houssein

Emam

Ali

2021

Neural Comput & Applic

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Coronavirus disease 2019 (COVID-19) is pervasive worldwide, posing a high risk to people’s safety and health. Many algorithms were developed to identify COVID-19. One way of identifying COVID-19 is by computed tomography (CT) images. Some segmentation methods are proposed to extract regions of interest from COVID-19 CT images to improve the classification. In this paper, an efficient version of the recent manta ray foraging optimization (MRFO) algorithm is proposed based on the oppositionbased learning called the MRFO-OBL algorithm. The original MRFO algorithm can stagnate in local optima and requires further exploration with adequate exploitation. Thus, to improve the population variety in the search space, we applied Opposition-based learning (OBL) in the MRFO’s initialization step. MRFO-OBL algorithm can solve the image segmentation problem using multilevel thresholding. The proposed MRFO-OBL is evaluated using Otsu’s method over the COVID-19 CT images and compared with six meta-heuristic algorithms: sine-cosine algorithm, moth flame optimization, equilibrium optimization, whale optimization algorithm, slap swarm algorithm, and original MRFO algorithm. MRFO-OBL obtained useful and accurate results in quality, consistency, and evaluation matrices, such as peak signal-to-noise ratio and structural similarity index. Eventually, MRFO-OBL obtained more robustness for the segmentation than all other algorithms compared. The experimental results demonstrate that the proposed method outperforms the original MRFO and the other compared algorithms under Otsu’s method for all the used metrics.

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Section: Literature Reviewmentioning

confidence: 99%

Improved manta ray foraging optimization for multi-level thresholding using COVID-19 CT images

Houssein

Emam

Ali

2021

Neural Comput & Applic

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“…where T is the threshold value, which is a predefined value or determined by a discriminating criterion, such as Kapur's entropy optimized with evolutionary algorithms [48], which is used to subdivide the original image into the target object and the background. e pixels with a gray level smaller than T are regarded as part of the background, and their gray levels are changed to "0."…”

Section: F-gei Calculation and Gait Features' Extractionmentioning

confidence: 99%

An Efficient Automatic Gait Anomaly Detection Method Based on Semisupervised Clustering

Yang

2021

Computational Intelligence and Neuroscience

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The aim of this work is to develop a common automatic computer method to distinguish human individuals with abnormal gait patterns from those with normal gait patterns. As long as the silhouette gait images of the subjects are obtainable, the proposed method is capable of providing online anomaly gait detection result without additional work on analyzing the gait features of the target subjects before ahead. Moreover, the proposed method does not need any parameter settings by users and can start producing detection results under the work by only collecting a very small number of gait samples, even though none of those gait samples are abnormal. Therefore, the proposed method can provide fast and simple deployment for various anomaly gait detection application scenarios. The proposed method is composed of two main modules: (1) feature extraction from gait images and (2) anomaly detection via binary classification. In the first module, a new representation of the most frequently involved area of the silhouette gait images called full gait energy image (F-GEI) is proposed. Furthermore, based on the F-GEI, a novel and simple method characterizing individual walking properties is developed to extract gait features from individual subjects. In the second module, based on the very limited prior knowledge on the target dataset, a semisupervised clustering algorithm is proposed to perform the binary classification for detecting the gait anomaly of each subject. The performance of the proposed gait anomaly detection method was evaluated on the human gaits dataset in comparison with three state-of-the-art methods. The experiment results show that the proposed method is an effective and efficient gait anomaly detection method in terms of accuracy, robustness, and computational efficiency.

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“…The DMS-QPSO approach is based on the QPSO algorithm's normal variant. The distinction between both the QPSO and DMS-QPSO algorithms is that the swarms are dynamic and lower in size [24]. The DMS-QPSO algorithm's neighbourhood topology has two essential features; The DMS splits the entire QPSO algorithm population into tiny swarms.…”

Section: Dms Of Populationmentioning

confidence: 99%

A Fast Quantum Particle Swarm Optimization Algorithm for Image Denoising Problem

Elsayed¹,

Salem²,

Aly³

2020

IJIES

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Noise can affect images while acquired, transmitted, stored or compressed. One of the best methods for noise removal is the sparse representation algorithm (SR). The Quantum Particle Swarm Optimization (QPSO) is one of the meta-heuristic algorithms. This paper shows excellent results in noise reduction in the quick version of QPSO, which uses benefit of the SRs and meta-heuristic algorithms. This approach is known as FQPSO-MP, depending on the matching pursuit algorithm (MP). A proposed Dynamic-Multi-Swarm (DMS) and a pre-learned dictionary (FQPSO-MP) method saves the time of calculating the learning dictionary. These modifications contribute to important benefits of computing efficiency (productivity improvements of approximately 90% are achieved) without sacred image quality in comparison with the initial QPSO-MP technique (the bigger reduction relative to the PSNR indexes is lower than 0.58 dB and 0.019). The proposed FQPSO-MP method compared to the original QPSO-MP method after modification. The scientific results show that the FQPSO-MP algorithm is more effective and quicker without sacrificing image quality than the FQPSO-MP algorithm. The experimental results show, in comparison to state-of-the-art denoising algorithms, that both quantitative and image quality results are achieved with the suggested FQPSO-MP method.

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A non-revisiting quantum-behaved particle swarm optimization based multilevel thresholding for image segmentation

Cited by 31 publications

References 57 publications

Improved manta ray foraging optimization for multi-level thresholding using COVID-19 CT images

Improved manta ray foraging optimization for multi-level thresholding using COVID-19 CT images

An Efficient Automatic Gait Anomaly Detection Method Based on Semisupervised Clustering

A Fast Quantum Particle Swarm Optimization Algorithm for Image Denoising Problem

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