K-Means Segmentation of Underwater Image Based on Improved Manta Ray Algorithm

Zhu, Donglin; Xie, Linpeng; Zhou, Changjun

doi:10.1155/2022/4587880

Cited by 6 publications

(3 citation statements)

References 56 publications

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“…The flow chart of the K-means clustering algorithm is shown in Figure 3 [4]; in the original Kmeans clustering, there are two major problems, one is the selection of the initial point, and the other is the number of classes of clusters, both of which are not properly selected will directly affect the clustering results, so this paper selects the elbow method combined with the SC contour coefficient using the original K-means clustering to determine the best number of clusters, and uses genetic algorithm to optimize the selection of the initial The location of the center point is optimized, and then the clustering is carried out for high potassium glass and lead-barium glass respectively [5]. The reason for choosing genetic algorithm to optimize k-means instead of k-means++ is that according to the literature [1] it is known that genetic algorithm optimizes k-means with about 10% higher accuracy than k-means++ [6].…”

Section: Limitations Of Original K-means Clusteringmentioning

confidence: 99%

Construction of glass composition analysis and identification model based on K-means algorithm optimized by genetic algorithm

Zhang¹,

Wu²,

Zhou³

2023

HSET

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Ancient glass is highly susceptible to weathering by the environment in which it is buried. During the weathering process the proportion of its composition changes, thus affecting the correct judgment of its category. In this paper, the two categories of high potassium and lead-barium glass were subclassified separately, and firstly, the elbow method was used to combine the SC contour coefficients to obtain their optimal number of clusters as 3,7 respectively; after that, the initial point selection was set as the decision variable, the interclass distance and the minimum as the fitness function, and the initial population size was set as 50, the clustering distance as Euclidean distance and other parameters, and the genetic algorithm was used to optimize the K-means clustering to achieve the subclassification delineation. Taking lead barium as an example, after optimization, CHI is increased by 4, DBI is decreased by 0.3, contour coefficient is increased by 0.2, and the clustering effect becomes better. Finally, the Kappa consistency test was performed, and the Kappa coefficient was 0.822 indicating the reasonableness of the clustering results; by changing the selected feature vector dimensions for sensitivity analysis, the Kappa coefficients were all above 0.8, which were not sensitive to the dimensionality of the feature vectors. The model effectively implements the problem of subclassifying different classes of glass for subclassification.

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Section: Limitations Of Original K-means Clusteringmentioning

confidence: 99%

Construction of glass composition analysis and identification model based on K-means algorithm optimized by genetic algorithm

Zhang¹,

Wu²,

Zhou³

2023

HSET

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“…Zhu et al [34] proposed a k-means-based image segmentation approach using an IMRFO algorithm. The IMRFO uses a Levy-flight mechanism to enhance the flexibility of individual manta rays and present a random walk strategy to stop the algorithm from local optimum entrapment.…”

Section: ) Mrfo With Levy-flight Mechanismmentioning

confidence: 99%

Manta Ray Foraging Optimization Algorithm: Modifications and Applications

et al. 2023

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The novel metaheuristic manta ray foraging optimization (MRFO) algorithm is based on the smart conduct of manta rays. The MRFO algorithm is a newly developed swarm-based metaheuristic approach that emulates the supportive conduct performed by manta rays in search of food. The MRFO algorithm efficiently resolves several optimization difficulties in various domains due to its ability to provide an equilibrium between global and local searches during the search procedure, resulting in nearly optimal results. Thus, researchers have developed several variants of MRFO since its introduction. This paper provides an in-depth examination of recent MRFO research. First, the paper introduces the natural inspiration context of MRFO and its conceptual optimization framework, and then MRFO modifications, hybridizations, and applications across different domains are discussed. Finally, a meta-analysis of the developments of the MRFO is presented along with the possible future research directions. This study can be useful for researchers and practitioners in optimization, engineering design, machine learning, scheduling, image processing, and other fields.

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“…To resolve these difficulties, several improved clustering algorithms have been proposed [ 13 , 14 , 15 , 16 ]. The K-means method divides each sample into a specific cluster.…”

Section: Introductionmentioning

confidence: 99%

A Robust and High-Dimensional Clustering Algorithm Based on Feature Weight and Entropy

2023

Entropy

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Since the Fuzzy C-Means algorithm is incapable of considering the influence of different features and exponential constraints on high-dimensional and complex data, a fuzzy clustering algorithm based on non-Euclidean distance combining feature weights and entropy weights is proposed. The proposed algorithm is based on the Fuzzy C-Means soft clustering algorithm to deal with high-dimensional and complex data. The objective function of the new algorithm is modified with the help of two different entropy terms and a non-Euclidean way of computing the distance. The distance calculation formula enhances the efficiency of extracting the contribution of different features. The first entropy term helps to minimize the clusters’ dispersion and maximize the negative entropy to control the clustering process, which also promotes the association between the samples. The second entropy term helps to control the weights of features since different features have different weights in the clustering process. Experiments on real-world datasets indicate that the proposed algorithm gives better clustering results than other algorithms. The experiments demonstrate the proposed algorithm’s robustness by analyzing the parameters’ sensitivity and comparing the computational distance formulas. In summary, the improved algorithm improves classification performance under noisy interference and high-dimensional datasets, increases computational efficiency, performs well in real-world high-dimensional datasets, and encourages the development of robust noise-resistant high-dimensional fuzzy clustering algorithms.

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K-Means Segmentation of Underwater Image Based on Improved Manta Ray Algorithm

Cited by 6 publications

References 56 publications

Construction of glass composition analysis and identification model based on K-means algorithm optimized by genetic algorithm

Construction of glass composition analysis and identification model based on K-means algorithm optimized by genetic algorithm

Manta Ray Foraging Optimization Algorithm: Modifications and Applications

A Robust and High-Dimensional Clustering Algorithm Based on Feature Weight and Entropy

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