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Grey prediction evolution algorithm with a dominator guidance strategy for solving multi-level image thresholding

APPLIED SOFT COMPUTING 2025年 174卷

作者： Yang, Peixin Hu, Zhongbo Zhou, Yang Su, Qinghua Xiong, Wentao Yangtze Univ Sch Informat & Math Jingzhou Hubei Peoples R China Hubei Engn Univ Sch Math & Stat Xiaogan Hubei Peoples R China

multi-level thresholding (MLT) stands as a pivotal method for extracting target information from images. Meta-heuristic algorithms provide an efficient way to implement MLT and retains more research space for accuracy optimization of high-dimensional multi-level thresholding (HDMLT) of images than they do for low-dimensional multi-level thresholding (LDMIT). In order to improve the algorithmic accuracy in solving the high-dimensional problems, a grey prediction evolution algorithm with a dominator guidance strategy (GPEdg) is proposed in this paper. GPEdg employs Otsu's method as its objective function to find the best threshold configuration. The novel operator in the algorithm, i.e., a dominator guidance (dg) strategy, uses a linear combination of three difference vectors to guide the top 50% individuals of populations to learn from the top 20% of them. An efficient balance of search abilities suitable for solving HDMLT problems is expected to be achieved by injecting the local search capability of the dg strategy into GPE's powerful global search capability. Furthermore, a thresholding morphological profile based method (TMP) leverages the thresholding results generated by GPEdg to train a support vector machine (SVM) for hyperspectral image classification. Numerical experiments are conducted for the newly proposed algorithm and five state-of-the-art algorithms on three image datasets to compare the performance in six metrics, i.e., peak signal-to-noise ratio, structural similarity index, features similarity index, objective function value, stability and time consumption. Overall accuracy and average accuracy are tested on two commonly used hyperspectral image data. The results show that GPEdg exhibits outstanding thresholding performance while TMP enhances the classification accuracy of these images. If this paper is accepted, Matlab_codes associated with this paper will be uploaded to https://***/Zhongbo-Hu/Prediction-Evolutionary-Algorithm-HOMEPAGE

关键词： multi-level image thresholding Evolution algorithm Otsu's method Hyperspectral image classification

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multi-level image thresholding based on Improved Fireworks Algorithm 13

Multi-level Image Thresholding based on Improved Fireworks A...

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13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)

作者： Ma, Miao Zheng, Weige Wu, Jie Yang, Kaifang Guo, Min Minist Educ Key Lab Modern Teaching Technol Xian Shaanxi Peoples R China Shaanxi Normal Univ Sch Comp Sci Xian Shaanxi Peoples R China

ISBN: (纸本)9781538621653

Aiming at achieving the optimal multi-level thresholding quickly and effectively for the image segmentation, this paper proposes an improved fireworks algorithm based image segmentation method. The proposed method transforms the multi-level thresholding problem into a multivariate combinational optimization problem and then improved the fireworks algorithm for a better image segmentation. Meanwhile, the Otsu method is adopted as the fitness function, and the global search and the local search in the improved fireworks algorithm method is utilized to achieve multi-level thresholding concurrently and efficiently. The experimental results show that the improved fireworks algorithm based image segmentation method can significantly improve the segmentation efficiency comparing with other swarm intelligence algorithm based image segmentation methods.

关键词： multi-level image thresholding fireworks algorithm image segmentation the Otsu method

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A Novel Biologically Inspired Approach for Clustering and multi-level image thresholding: Modified Harris Hawks Optimizer

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COGNITIVE COMPUTATION 2022年第3期14卷 955-969页

作者： Cai, Jia Luo, Tianhua Xu, Guanglong Tang, Yi Guangdong Univ Finance & Econ Sch Digital Econ Guangzhou 510320 Guangdong Peoples R China Guangdong Univ Finance & Econ Sch Stat & Math Guangzhou 510320 Guangdong Peoples R China South China Univ Technol Sch Econ & Finance Guangzhou 510641 Guangdong Peoples R China Yunnan Minzu Univ Sch Math & Comp Sci Kunming 650500 Yunnan Peoples R China

Biologically inspired computing deals with complex real-world problems using elegantly modeled techniques motivated by the behaviors of creatures in nature. Harris hawks optimizer (HHO), motivated by the cooperative behavior and hunting style of Harris' hawks, is a nature-inspired optimization paradigm. As an eminent swarm intelligence method, HHO has established strong performance. However, the original HHO may face difficulties when handling practical multimodal and composition problems. To overcome these challenges, this paper investigates an improved HHO, which considers nonlinear decay energy, introduces the grey wolf optimizer (GWO) as a competitive method to modify conventional HHO, and improves the balance between its exploration and exploitation. The proposed approach combines different cognitive hunting behaviors of Harris' hawks and grey wolf packs. The main idea of the proposed method can be described as follows: First, we generate a set of candidate solutions and then divide them into two halves. The improved HHO is employed to update the solutions in the first half, while the search phase of GWO is introduced to update the solutions in the second half. Second, we choose the best solutions for the union subpopulations and continue to conduct the iteration procedure. Furthermore, the new approach is utilized to solve the clustering problem and determine the optimal threshold values for multi-level image segmentation problems. Experimental results on 11 benchmark functions illustrate the effectiveness of the proposed approach. Extensive results on clustering and multi-level image segmentation demonstrate the efficiency of the proposed algorithm.

关键词： Grey wolf optimizer Harris hawks optimizer Clustering multi-level image thresholding

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Rough-Fuzzy Collaborative multi-level image thresholding: A Differential Evolution Approach 21st

Rough-Fuzzy Collaborative Multi-level Image Thresholding: A ...

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21st International Conference on Soft Computing (MENDEL)

作者： Paul, Sujoy Datta, Shounak Das, Swagatam Jadavpur Univ Dept Elect & Telecommun Engn Kolkata 700032 India Indian Stat Inst Elect & Commun Sci Unit Kolkata 700108 India

ISBN: (纸本)9783319198248;9783319198231

In this article, a granular computing based multi-level gray image thresholding algorithm is presented. An image is divided into spatial blocks called granules, and the classes of gray levels are represented using a fuzzy-rough collaborative approach, where the measure of roughness of a rough set is also modified from the classical definition of rough sets. This measure for each rough set is minimized simultaneously to obtain the optimal thresholds. Tchebycheff decomposition approach is employed to transform this multi-objective optimization problem to a single objective optimization problem. Differential Evolution (DE), one of the most efficient evolutionary optimizers of current interest, is used to optimize this single objective function, thus reducing the execution time. Superiority of the proposed method is presented by comparing it with some popular image thresholding techniques. MSSIM index and Probabilistic Rand Index (PRI) are used for quantitative comparison on the Berkley image Segmentation Data Set (BSDS300).

关键词： Rough sets Fuzzy sets multi-level image thresholding Fuzzy image thresholding Differential evolution Tchebycheff approach

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New Quantum Inspired Tabu Search for multi-level Colour image thresholding

New Quantum Inspired Tabu Search for Multi-level Colour Imag...

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8th International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Dey, Sandip Bhattacharyya, Siddhartha Maulik, Ujjwal Camellia Inst Technol Dept Informat Technol Kolkata 700129 India RCC Inst Informat Technol Dept Informat Technol Kolkata 700015 India Jadavpur Univ Dept Comp Sci & Engn Kolkata 700032 India

ISBN: (纸本)9789380544120

In this article, a Quantum Inspired Tabu Search for multi-level thresholding for Colour image has been developed to boost the possible effectiveness than that of its classical counterpart. The proposed algorithm has been applied to two true colour images to determine optimal threshold values at different levels using Otsu's method as an objective function. The features of quantum mechanics are coupled with the basic constitution of a popular meta-heuristic algorithm, called tabu search to form the quantum inspired meta-heuristic algorithm. Between the participating algorithms, the proposed algorithm takes least time for execution. The usefulness of the proposed method is established in context of exactitude, resilience and computational time over its respective conventional method. In addition, a popular test, called one-tailed t-test, used for statistical measurement, demonstrates the efficiency of the proposed algorithm.

关键词： colour image segmentation multi-level image thresholding tabu search quantum computing Otsu's method

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multi-level Iris Video image thresholding

Multi-level Iris Video Image Thresholding

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IEEE Workshop on Computational Intelligence in Biometrics - Theory, Algorithms and Applications

作者： Du, Yingzi Thomas, N. Luke Arslanturk, Emrah Indiana Univ Purdue Univ Biometr & Pattern Recognit Lab Dept Elect & Comp Engn Indianapolis IN 46202 USA

ISBN: (纸本)9781424427734

Iris recognition has been shown to be one of the most accurate biometrics. However, under non-ideal situations, its recognition accuracy can be reduced dramatically. Under such situations, video images can be used to improve the accuracy. The traditional single image based segmentation method could be inefficient. Video image based thresholding method can help improve the segmentation efficiency. However, traditional thresholding methods are not designed for iris images. In this paper, the multi-level iris video image thresholding method is proposed. It takes advantage of the correlations between consecutive images for video based thresholding. It is an orientation invariant thresholding scheme. K-mean clustering is used to find the clusters and PCA is used to quickly project the image to the clusters. The experimental results show the proposed method is effective. The thresholded images show clear pupil and iris areas, which can help further iris segmentation and processing. In addition, the proposed method can be applied to non-video images if they are obtained from the same sensor with similar illumination conditions.

关键词： Iris recognition video image multi-level image thresholding multi-level iris video image thresholding

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Automatic multilevel image thresholding segmentation using hybrid bio-inspired algorithm and artificial neural network for histopathology images

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multiMEDIA TOOLS AND APPLICATIONS 2023年第4期82卷 4979-5010页

作者： Vijh, Surbhi Saraswat, Mukesh Kumar, Sumit Amity Univ ASET Noida India Jaypee Inst Informat Technol Noida India Amity Univ Noida India

Automated medical imagining is growing rapidly for advanced clinical treatment and intervention in medical diagnosis. The segmentation of nuclei in digital histopathology is considered the most crucial aspect in diagnosis and evaluating the severity of disease. Therefore, in this paper, an automated nuclei segmentation method has been introduced for the histopathological images. The proposed segmentation method uses a new hybrid algorithm of lion optimization and cat swarm optimization to provide an optimal threshold value for efficient multi-level image thresholding segmentation. Moreover, a new fitness function using Otsu's function and Yager's entropy has also been presented for robust results. Extensive simulations are examined to determine the performance of the proposed hybrid algorithm. Firstly, evaluation on CEC 2017 benchmark suite are performed using three quality metrics, namely average fitness value, peak signal-to-noise ratio, and structural similarity index. Secondly, for the performance analysis of developed segmentation method, two Histopathological data sets are used, namely breast Histopathological images and lung Histopathological images. Furthermore, to find the optimum number of clusters, artificial neural network has been used and its performance is measured in terms of the accuracy, sensitivity, and specificity. The classifier returned an accuracy of 71.4%, sensitivity of 93.23%, and specificity of 93.12% for breast Histopathological images. For lung Histopathological images, the accuracy, sensitivity, and specificity were 66.17%, 89.53%, and 90.62% respectively. Moreover, the proposed method is also validated using the ground truth images, acquired from expert pathologist, by calculating dice coefficient and Jaccard coefficient. The findings and observation show that the proposed algorithm provides promising and significant result.

关键词： Medical imaging multi-level image thresholding Lion optimization Cat swarm optimization Artificial neural network

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A Novel Qutrit Based Quantum Ant Colony Optimization for multi-level thresholding

A Novel Qutrit Based Quantum Ant Colony Optimization for Mul...

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IEEE Region 10 Conference on Technology, Knowledge, and Society (TENCON)

作者： Bhattacharyya, Siddhartha Dey, Sandip Konar, Debanjan RCC Inst Informat Technol Dept Informat Technol Kolkata India Sukanta Mahavidyalaya Dept Comp Sci Dhupguri Jalpaiguri India Sikkim Manipal Univ Sikkim Sikkim Manipal Inst Technol Dept Comp Sci & Enineering Gangtok India

ISBN: (纸本)9781728118956

The paper presents an efficient algorithm for image thresholding based on multi-level thresholding. The proposed algorithm employs an effective image thresholding approach as fitness function to compute the threshold. A novel qutrit based quantum algorithm is designed based on the lifestyle of real ants. The novel features of qutrit are incorporated in association with ant colony optimization in the proposed algorithm. The efficacy of the proposed algorithm is exhibited by comparing it with two other popular algorithms in reference with optimal threshold values at different levels, optimal fitness, their mean and standard deviation and the computational time. Moreover, an uniformity measure is introduced to judge the quality of thresholding among different participants at various levels. Furthermore, the Friedman test among all algorithms is performed to statistically establish the supremacy of the proposed algorithm. Finally, the efficacy of the proposed algorithm is visually established by presenting convergence curves for higher levels of thresholding for four test images.

关键词： Qutrit Friedman test Otsu's function multi-level image thresholding Ant Colony Optimization

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multilevel image Segmentation Using Modified Red Deer Algorithm 11

Multilevel Image Segmentation Using Modified Red Deer Algori...

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11th International Conference on Cloud Computing, Data Science and Engineering (Confluence)

作者： Dey, Sandip De, Sourav Deb, Abhirup Debnath, Soumyaratna Sukanta Mahavidyalaya Dept Comp Sci Dhupguri Jalpaiguri India Cooch Behar Govt Engg Coll Dept CSE Cooch Behar India

ISBN: (纸本)9781665414517

In this paper, an evolution strategy based, Modified Red Deer Algorithm (MRDA) has been proposed to find optimum thresholds of publicly available gray scale images. In the recent year, the activity of red deers has been thoroughly observed in the course of their breading season by a group of researchers. Later, this inspired them to introduce a renowned meta-heuristic, popularly known as Red Deer Algorithm (RDA). The RDA is capable to handle several combinatorial optimization problems of various real-world applications. In this paper, a number of adaptive strategy has been suggested to alter the intrinsic operators and parameters used in RDA to improve its performance. The efficiency of MRDA has been judged with RDA and classical Particle Swarm Optimization (PSO) using two publicly accessible real world benchmark images. The performance of each of the competitive method has been analysed with regards to a variety of measure quantitatively and visually. Finally, as a statistical comparison, Kruskal-Wallis test has been carried out between the proposed method and others. The obtained results prove that MRDA is the best performing method than others in all aspects and provides immensely competitive results.

关键词： Red Deer Algorithm Particle Swarm Optimization optimization multi-level image thresholding Kruskal-Wallis test

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