Fuzzy Multilevel Image Thresholding Based on Improved Coyote Optimization Algorithm

Li, Linguo; Sun, Lijuan; Xue, Yu; Li, Shujing; Huang, Xuwen; Mansour, Romany F.

doi:10.1109/access.2021.3060749

Cited by 56 publications

(52 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Structural Similarity (SSIM) is a common metric used to measure the structural similarity between the original image and the segmented image [ 3 ], and is defined as:

where μ I and μ Seg indicate the mean intensity of the original image and its segmented image; σ I and σ Seg denote the standard deviation of the original image and its segmented image; σ I,Seg is the covariance of the original image and the segmented image. c 1 and c 2 are constant.…”

Section: Experimental Results On Multilevel Thresholdingmentioning

confidence: 99%

“…The main goal of segmentation is to divide the image into homogeneous classes. The elements of each class share common attributes such as grayscale, feature, color, intensity, or texture [ 2 , 3 , 4 , 5 ]. In the literature, there are four standard image segmentation methods, which can be divided into (1) clustering-based methods, (2) region-based methods, (3) graph-based methods, (4) thresholding-based methods.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Enhanced Slime Mould Algorithm for Multilevel Thresholding Image Segmentation Using Entropy Measures

Lin

Jia

Abualigah

et al. 2021

Entropy

View full text Add to dashboard Cite

Image segmentation is a fundamental but essential step in image processing because it dramatically influences posterior image analysis. Multilevel thresholding image segmentation is one of the most popular image segmentation techniques, and many researchers have used meta-heuristic optimization algorithms (MAs) to determine the threshold values. However, MAs have some defects; for example, they are prone to stagnate in local optimal and slow convergence speed. This paper proposes an enhanced slime mould algorithm for global optimization and multilevel thresholding image segmentation, namely ESMA. First, the Levy flight method is used to improve the exploration ability of SMA. Second, quasi opposition-based learning is introduced to enhance the exploitation ability and balance the exploration and exploitation. Then, the superiority of the proposed work ESMA is confirmed concerning the 23 benchmark functions. Afterward, the ESMA is applied in multilevel thresholding image segmentation using minimum cross-entropy as the fitness function. We select eight greyscale images as the benchmark images for testing and compare them with the other classical and state-of-the-art algorithms. Meanwhile, the experimental metrics include the average fitness (mean), standard deviation (Std), peak signal to noise ratio (PSNR), structure similarity index (SSIM), feature similarity index (FSIM), and Wilcoxon rank-sum test, which is utilized to evaluate the quality of segmentation. Experimental results demonstrated that ESMA is superior to other algorithms and can provide higher segmentation accuracy.

show abstract

“…Structural Similarity (SSIM) is a common metric used to measure the structural similarity between the original image and the segmented image [ 3 ], and is defined as:

Section: Experimental Results On Multilevel Thresholdingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Enhanced Slime Mould Algorithm for Multilevel Thresholding Image Segmentation Using Entropy Measures

Lin

Jia

Abualigah

et al. 2021

Entropy

View full text Add to dashboard Cite

show abstract

“…Techniques like Fuzzy and Deep Learning are important in improving the quality of images related to the healthcare sector. The work was based on Fuzzy Multilevel Image thresholding using an improved Coyote Optimization Algorithm [ 54 ]. The techniques like deep reinforcement learning are employed in areas like anomaly detection that combine reinforcement learning and deep learning, which enables artificial agents to learn knowledge and experience actual data directly [ 55 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Empirical Study on Classifiers for Earlier Prediction of COVID-19 Infection Cure and Death Rate in the Indian States

Guleria¹,

Ahmed

Alhumam

et al. 2022

Healthcare

View full text Add to dashboard Cite

Machine Learning methods can play a key role in predicting the spread of respiratory infection with the help of predictive analytics. Machine Learning techniques help mine data to better estimate and predict the COVID-19 infection status. A Fine-tuned Ensemble Classification approach for predicting the death and cure rates of patients from infection using Machine Learning techniques has been proposed for different states of India. The proposed classification model is applied to the recent COVID-19 dataset for India, and a performance evaluation of various state-of-the-art classifiers to the proposed model is performed. The classifiers forecasted the patients’ infection status in different regions to better plan resources and response care systems. The appropriate classification of the output class based on the extracted input features is essential to achieve accurate results of classifiers. The experimental outcome exhibits that the proposed Hybrid Model reached a maximum F1-score of 94% compared to Ensembles and other classifiers like Support Vector Machine, Decision Trees, and Gaussian Naïve Bayes on a dataset of 5004 instances through 10-fold cross-validation for predicting the right class. The feasibility of automated prediction for COVID-19 infection cure and death rates in the Indian states was demonstrated.

show abstract

“…The hyperparameters of the MN are tuned using QOFFO algorithm. In general, Firefly algorithm (FA) is defined as a meta-heuristic model to solve the optimization issues [15][16][17][18][19][20][21][22]. The development of FA is applied in 3 ideas:…”

Section: Parameter Tuning Using Qoffomentioning

confidence: 99%

“…The evaluation measures utilized for determining the results are PSNR, SSIM, and accuracy. A set of methods used for comparative analysis are Markov Random Field (MRF), Markov Weight Field (MWF), Sparse Representation-based Global Search method (SRGS), Semi-Coupled Dictionary Learning method (SCDL), CNN and Modified CNN (MCNN), optimal DL with CNN [26][27][28][29][30][31][32]. Fig.…”

Section: Experimental Validationmentioning

confidence: 99%

Target Detection Algorithm in Crime Recognition Using Artificial Intelligence

Shin¹,

Kim²,

Lee³

2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

Presently, suspect prediction of crime scenes can be considered as a classification task, which predicts the suspects based on the time, space, and type of crime. Performing digital forensic investigation in a big data environment poses several challenges to the investigational officer. Besides, the facial sketches are widely employed by the law enforcement agencies for assisting the suspect identification of suspects involved in crime scenes. The sketches utilized in the forensic investigations are either drawn by forensic artists or generated through the computer program (composite sketches) based on the verbal explanation given by the eyewitness or victim. Since this suspect identification process is slow and difficult, it is required to design a technique for a quick and automated facial sketch generation. Machine Learning (ML) and deep learning (DL) models find it useful to automatically support the decision of forensics experts. The challenge is the incorporation of the domain expert knowledge with DL models for developing efficient techniques to make better decisions. In this view, this study develops a new artificial intelligence (AI) based DL model with face sketch synthesis (FSS) for suspect identification (DLFSS-SI) in a big data environment. The proposed method performs preprocessing at the primary stage to improvise the image quality. In addition, the proposed model uses a DL based MobileNet (MN) model for feature extractor, and the hyper parameters of the MobileNet are tuned by quasi oppositional firefly optimization (QOFFO) algorithm. The proposed model automatically draws the sketches of the input facial images. Moreover, a qualitative similarity assessment takes place with the sketch drawn by a professional artist by the eyewitness. If there is a higher resemblance between the two sketches, the suspect will be determined. To validate the effective performance of the DLFSS-SI method, a detailed qualitative and quantitative examination takes place. The experimental outcome stated that the DLFSS-SI model has outperformed the compared methods in terms of mean square error (MSE), peak signal to noise ratio (PSNR), average actuary, and average computation time.

show abstract

Fuzzy Multilevel Image Thresholding Based on Improved Coyote Optimization Algorithm

Cited by 56 publications

References 30 publications

Enhanced Slime Mould Algorithm for Multilevel Thresholding Image Segmentation Using Entropy Measures

Enhanced Slime Mould Algorithm for Multilevel Thresholding Image Segmentation Using Entropy Measures

Empirical Study on Classifiers for Earlier Prediction of COVID-19 Infection Cure and Death Rate in the Indian States

Target Detection Algorithm in Crime Recognition Using Artificial Intelligence

Contact Info

Product

Resources

About