基于混合适应度函数的混沌冠状病毒优化算法的多级阈值卫星图像分割。

IF 4.5 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Khalid M Hosny, Asmaa M Khalid, Hanaa M Hamza, Seyedali Mirjalili
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引用次数: 6

摘要

图像分割是数字图像处理应用的关键步骤。多级阈值分割是一种常用的图像分割方法,通过确定一组阈值将图像划分为不同的类别。然而,当所需的阈值较高时,计算复杂性会增加。为此,本文引入一种改进的冠状病毒优化算法进行图像分割。在该算法的初始化步骤中加入混沌映射的概念,增加了解的多样性。将常用的两种方法Otsu熵和Kapur熵混合,形成新的适应度函数来确定最优阈值。使用两个不同的数据集,包括六个基准和六个卫星图像,对所提出的算法进行了评估。采用各种评价指标,如均方误差、峰值信噪比、结构相似指数、特征相似指数和归一化相关系数等,来衡量使用该算法分割的图像的质量。此外,计算了最佳适应度值,以证明所提出的方法能够找到最优解。将所得结果与11种功能强大的最新元启发式算法进行了比较,证明了该算法在图像分割问题上的优越性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Multilevel thresholding satellite image segmentation using chaotic coronavirus optimization algorithm with hybrid fitness function.

Multilevel thresholding satellite image segmentation using chaotic coronavirus optimization algorithm with hybrid fitness function.

Multilevel thresholding satellite image segmentation using chaotic coronavirus optimization algorithm with hybrid fitness function.

Multilevel thresholding satellite image segmentation using chaotic coronavirus optimization algorithm with hybrid fitness function.

Image segmentation is a critical step in digital image processing applications. One of the most preferred methods for image segmentation is multilevel thresholding, in which a set of threshold values is determined to divide an image into different classes. However, the computational complexity increases when the required thresholds are high. Therefore, this paper introduces a modified Coronavirus Optimization algorithm for image segmentation. In the proposed algorithm, the chaotic map concept is added to the initialization step of the naive algorithm to increase the diversity of solutions. A hybrid of the two commonly used methods, Otsu's and Kapur's entropy, is applied to form a new fitness function to determine the optimum threshold values. The proposed algorithm is evaluated using two different datasets, including six benchmarks and six satellite images. Various evaluation metrics are used to measure the quality of the segmented images using the proposed algorithm, such as mean square error, peak signal-to-noise ratio, Structural Similarity Index, Feature Similarity Index, and Normalized Correlation Coefficient. Additionally, the best fitness values are calculated to demonstrate the proposed method's ability to find the optimum solution. The obtained results are compared to eleven powerful and recent metaheuristics and prove the superiority of the proposed algorithm in the image segmentation problem.

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来源期刊
Neural Computing & Applications
Neural Computing & Applications 工程技术-计算机:人工智能
CiteScore
11.40
自引率
8.30%
发文量
1280
审稿时长
6.9 months
期刊介绍: Neural Computing & Applications is an international journal which publishes original research and other information in the field of practical applications of neural computing and related techniques such as genetic algorithms, fuzzy logic and neuro-fuzzy systems. All items relevant to building practical systems are within its scope, including but not limited to: -adaptive computing- algorithms- applicable neural networks theory- applied statistics- architectures- artificial intelligence- benchmarks- case histories of innovative applications- fuzzy logic- genetic algorithms- hardware implementations- hybrid intelligent systems- intelligent agents- intelligent control systems- intelligent diagnostics- intelligent forecasting- machine learning- neural networks- neuro-fuzzy systems- pattern recognition- performance measures- self-learning systems- software simulations- supervised and unsupervised learning methods- system engineering and integration. Featured contributions fall into several categories: Original Articles, Review Articles, Book Reviews and Announcements.
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