Multilevel Color Image Segmentation Based on GLCM and Improved Salp Swarm Algorithm

Xing, Zhikai; Jia, Heming

doi:10.1109/access.2019.2904511

Cited by 72 publications

(32 citation statements)

References 51 publications

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“…5 shows the direction analysis of GLCM with a simple example. Subsequently, GLCM has shown powerful ability on automatic texture discrimination in [36][37][38]. However, it is not an easy job to balance the matrix performance and the window size.…”

Section: ) Co-occurrence Matrixmentioning

confidence: 99%

Automated Visual Defect Detection for Flat Steel Surface: A Survey

Luo

Fang

Liu

et al. 2020

IEEE Trans. Instrum. Meas.

339

111

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Automated computer-vision-based defect detection has received much attention with the increasing surface quality assurance demands for the industrial manufacturing of flat steels. This paper attempts to present a comprehensive survey on surface defect detection technologies by reviewing about 120 publications over the last two decades for three typical flat steel products of con-casting slabs, hot-and cold-rolled steel strips. According to the nature of algorithms as well as image features, the existing methodologies are categorized into four groups: Statistical, spectral, model-based and machine learning. These literatures are summarized in this review to enable easy referral to suitable methods for diverse application scenarios in steel mills. Realization recommendations and future research trends are also addressed at an abstract level.

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Section: ) Co-occurrence Matrixmentioning

confidence: 99%

Automated Visual Defect Detection for Flat Steel Surface: A Survey

Luo

Fang

Liu

et al. 2020

IEEE Trans. Instrum. Meas.

339

111

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show abstract

“…Adiknya dua orang, yaitu Petruk dan Bagong. Bapak beranak ini adalah Punakawan dalam dunia pewayangan, mengabdi pada ksatria yang berpihak pada kebenaran (Wulandari, D., 2017). Para pakar muslim sepakat bahwa nala gareng adalah sebuah kata bahasa Arab yang dijawakan.…”

Section: A Wayangunclassified

Wayang Image Classification Using SVM Method and GLCM Feature Extraction

Muhathir

Santoso²,

Larasati³

2021

JITE

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Wayang is a masterpiece of art that has been able to survive centuries of change and development as a reflection of life for the majority of society. Wayang has a high value because it does not only function as a "entertainment" spectacle, but also has many lessons and life values that can be learned from a wayang show. Puppet itself has various types and forms, and these forms have their own uniqueness, because of the many types of Puppet, many people do not know all the names and types of wayang. Therefore, in this research, we will discuss how to recognize wayang objects based on wayang images using the SVM and GLCM methods as feature extraction. The results showed that the classification of wayang using the SVM (Support Vector Machine) method and the GLCM (Gray Level Co-Occurrence Matrix) feature extraction can recognize wayang objects based on wayang images and classify them quite accurately and a maximum total accuracy of 83.2% is obtained.

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“…As can be observed from Table 21 that CISSA have indicated an obvious advantage over the other 7 comparative algorithms on solving redundant container deployment problem. For example, in scenario 1, the best value obtained by CISSA is 2180, and the corresponding terminal devices number of containers are (5,2,2,5,6,6,3,5,3,9,6,9,3,9,5,8,3,10,1,2,9,4,9,6).…”

Section: Experiments Of Cissa On Redundant Container Deployment Modelmentioning

confidence: 99%

“…Salp swarm algorithm (SSA) is a population-based metaheuristic optimization algorithm proposed by Mirjalili et al in 2017 [5], which mimics the predatory behavior of salp swarm. The algorithm is easy to implement due to the only main controlling parameter.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Improved Salp Swarm Algorithm on Redundant Container Deployment Problem

Hóng

Zhu

et al. 2019

IEEE Access

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As a representative of lightweight virtualization, container technology has been widely used in cloud services and edge computing applications. However, in the resource pool scenario composed by multiple intelligent terminal devices, considering the limited resources and poor stability of these devices, it is necessary to split the overall service into multiple microservices and deploy the backups of them in respective containers. Traditional container scheduling policies tend to be less effective in solving such problems. Therefore, the article used a meta-heuristic algorithm to solve this kind of problems. Based on a newly proposed salp swarm algorithm (SSA), the paper presented a comprehensive improved SSA (CISSA). CISSA improved the performance of the original SSA by 4 steps. To verify the performance of CISSA in different kinds of test functions, the algorithm was compared with 7 commonly-used meta-heuristic algorithms in 29 benchmark functions provided by the author of SSA. In addition, the article constructed three container cluster models of different sizes, all these algorithms were used to solve these redundant container deployment problems, the experimental results indicate that the CISSA is superior to other algorithms in such problems of different dimensions.

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Multilevel Color Image Segmentation Based on GLCM and Improved Salp Swarm Algorithm

Cited by 72 publications

References 51 publications

Automated Visual Defect Detection for Flat Steel Surface: A Survey

Automated Visual Defect Detection for Flat Steel Surface: A Survey

Wayang Image Classification Using SVM Method and GLCM Feature Extraction

A Comprehensive Improved Salp Swarm Algorithm on Redundant Container Deployment Problem

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