Nature-inspired optimization algorithms and their significance in multi-thresholding image segmentation: an inclusive review.

IF 2.7 4区计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Evolving Systems Pub Date : 2022-01-01 Epub Date: 2022-02-21 DOI:10.1007/s12530-022-09425-5

Rebika Rai, Arunita Das, Krishna Gopal Dhal

引用次数: 19

Abstract

Multilevel Thresholding (MLT) is considered as a significant and imperative research field in image segmentation that can efficiently resolve difficulties aroused while analyzing the segmented regions of multifaceted images with complicated nonlinear conditions. MLT being a simple exponential combinatorial optimization problem is commonly phrased by means of a sophisticated objective function requirement that can only be addressed by nondeterministic approaches. Consequently, researchers are engaging Nature-Inspired Optimization Algorithms (NIOA) as an alternate methodology that can be widely employed for resolving problems related to MLT. This paper delivers an acquainted review related to novel NIOA shaped lately in last three years (2019-2021) highlighting and exploring the major challenges encountered during the development of image multi-thresholding models based on NIOA.

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自然启发的优化算法及其在多阈值图像分割中的意义：综述。

多级阈值（MLT）是图像分割中一个重要而迫切的研究领域，它可以有效地解决在分析复杂非线性条件下的多面图像分割区域时遇到的困难。MLT是一个简单的指数组合优化问题，通常通过复杂的目标函数需求来表达，而这只能通过不确定性方法来解决。因此，研究人员正在将自然启发优化算法（NIOA）作为一种可广泛用于解决MLT相关问题的替代方法。本文对最近三年（2019-2021）形成的新型NIOA进行了熟悉的综述，强调并探索了在开发基于NIOA的图像多阈值模型过程中遇到的主要挑战。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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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.