Chimp optimization algorithm in multilevel image thresholding and image clustering.

IF 2.7 4区计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Evolving Systems Pub Date : 2022-05-27 DOI:10.1007/s12530-022-09443-3

Zubayer Kabir Eisham, Md Monzurul Haque, Md Samiur Rahman, Mirza Muntasir Nishat, Fahim Faisal, Mohammad Rakibul Islam

{"title":"Chimp optimization algorithm in multilevel image thresholding and image clustering.","authors":"Zubayer Kabir Eisham, Md Monzurul Haque, Md Samiur Rahman, Mirza Muntasir Nishat, Fahim Faisal, Mohammad Rakibul Islam","doi":"10.1007/s12530-022-09443-3","DOIUrl":null,"url":null,"abstract":"<p><p>Multilevel image thresholding and image clustering, two extensively used image processing techniques, have sparked renewed interest in recent years due to their wide range of applications. The approach of yielding multiple threshold values for each color channel to generate clustered and segmented images appears to be quite efficient and it provides significant performance, although this method is computationally heavy. To ease this complicated process, nature inspired optimization algorithms are quite handy tools. In this paper, the performance of Chimp Optimization Algorithm (ChOA) in image clustering and segmentation has been analyzed, based on multilevel thresholding for each color channel. To evaluate the performance of ChOA in this regard, several performance metrics have been used, namely, Segment evolution function, peak signal-to-noise ratio, Variation of information, Probability Rand Index, global consistency error, Feature Similarity Index and Structural Similarity Index, Blind/Referenceless Image Spatial Quality Evaluatoe, Perception based Image Quality Evaluator, Naturalness Image Quality Evaluator. This performance has been compared with eight other well known metaheuristic algorithms: Particle Swarm Optimization Algorithm, Whale Optimization Algorithm, Salp Swarm Algorithm, Harris Hawks Optimization Algorithm, Moth Flame Optimization Algorithm, Grey Wolf Optimization Algorithm, Archimedes Optimization Algorithm, African Vulture Optimization Algorithm using two popular thresholding techniques-Kapur's entropy method and Otsu's class variance method. The results demonstrate the effectiveness and competitive performance of Chimp Optimization Algorithm.</p>","PeriodicalId":12174,"journal":{"name":"Evolving Systems","volume":"54 1","pages":"1-44"},"PeriodicalIF":2.7000,"publicationDate":"2022-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9135988/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Evolving Systems","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s12530-022-09443-3","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}

引用次数: 0

Abstract

Multilevel image thresholding and image clustering, two extensively used image processing techniques, have sparked renewed interest in recent years due to their wide range of applications. The approach of yielding multiple threshold values for each color channel to generate clustered and segmented images appears to be quite efficient and it provides significant performance, although this method is computationally heavy. To ease this complicated process, nature inspired optimization algorithms are quite handy tools. In this paper, the performance of Chimp Optimization Algorithm (ChOA) in image clustering and segmentation has been analyzed, based on multilevel thresholding for each color channel. To evaluate the performance of ChOA in this regard, several performance metrics have been used, namely, Segment evolution function, peak signal-to-noise ratio, Variation of information, Probability Rand Index, global consistency error, Feature Similarity Index and Structural Similarity Index, Blind/Referenceless Image Spatial Quality Evaluatoe, Perception based Image Quality Evaluator, Naturalness Image Quality Evaluator. This performance has been compared with eight other well known metaheuristic algorithms: Particle Swarm Optimization Algorithm, Whale Optimization Algorithm, Salp Swarm Algorithm, Harris Hawks Optimization Algorithm, Moth Flame Optimization Algorithm, Grey Wolf Optimization Algorithm, Archimedes Optimization Algorithm, African Vulture Optimization Algorithm using two popular thresholding techniques-Kapur's entropy method and Otsu's class variance method. The results demonstrate the effectiveness and competitive performance of Chimp Optimization Algorithm.

查看原文本刊更多论文

多层图像阈值分割和图像聚类中的黑猩猩优化算法。

多层图像阈值分割和图像聚类是近年来应用广泛的两种图像处理技术，引起了人们的广泛关注。为每个颜色通道生成多个阈值来生成聚类和分割图像的方法似乎非常有效，并且提供了显著的性能，尽管这种方法的计算量很大。为了简化这个复杂的过程，自然启发的优化算法是非常方便的工具。本文分析了黑猩猩优化算法（ChOA）在图像聚类和分割中的性能，该算法基于对每个颜色通道进行多级阈值分割。为了评估ChOA在这方面的性能，使用了几个性能指标，即片段进化函数，峰值信噪比，信息变异，概率兰德指数，全局一致性误差，特征相似指数和结构相似指数，盲/无参考图像空间质量评价，基于感知的图像质量评价器，自然图像质量评价器。利用kapur的熵值法和Otsu的类方差法这两种流行的阈值技术，与粒子群优化算法、鲸鱼优化算法、Salp群算法、哈里斯鹰优化算法、蛾焰优化算法、灰狼优化算法、阿基米德优化算法、非洲秃鹫优化算法等八种著名的元启发式算法进行了比较。结果证明了黑猩猩优化算法的有效性和竞争力。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Evolving Systems COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-

CiteScore

7.80

自引率

6.20%

发文量

期刊介绍： Evolving Systems covers surveys, methodological, and application-oriented papers in the area of dynamically evolving systems. ‘Evolving systems’ are inspired by the idea of system model evolution in a dynamically changing and evolving environment. In contrast to the standard approach in machine learning, mathematical modelling and related disciplines where the model structure is assumed and fixed a priori and the problem is focused on parametric optimisation, evolving systems allow the model structure to gradually change/evolve. The aim of such continuous or life-long learning and domain adaptation is self-organization. It can adapt to new data patterns, is more suitable for streaming data, transfer learning and can recognise and learn from unknown and unpredictable data patterns. Such properties are critically important for autonomous, robotic systems that continue to learn and adapt after they are being designed (at run time). Evolving Systems solicits publications that address the problems of all aspects of system modelling, clustering, classification, prediction and control in non-stationary, unpredictable environments and describe new methods and approaches for their design. The journal is devoted to the topic of self-developing, self-organised, and evolving systems in its entirety — from systematic methods to case studies and real industrial applications. It covers all aspects of the methodology such as Evolving Systems methodology Evolving Neural Networks and Neuro-fuzzy Systems Evolving Classifiers and Clustering Evolving Controllers and Predictive models Evolving Explainable AI systems Evolving Systems applications but also looking at new paradigms and applications, including medicine, robotics, business, industrial automation, control systems, transportation, communications, environmental monitoring, biomedical systems, security, and electronic services, finance and economics. The common features for all submitted methods and systems are the evolving nature of the systems and the environments. The journal is encompassing contributions related to: 1) Methods of machine learning, AI, computational intelligence and mathematical modelling 2) Inspiration from Nature and Biology, including Neuroscience, Bioinformatics and Molecular biology, Quantum physics 3) Applications in engineering, business, social sciences.