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Linearized circular energy curve color image segmentation based on Tsallis entropy

EXPERT SYSTEMS WITH APPLICATIONS 2025年 273卷

作者： Wang, Shaoxun Fan, Jiulun Liu, Heng Xi An Jiao Tong Univ Sch Comp Sci & Technol Xian 710049 Peoples R China Xian Univ Posts & Telecommun Sch Commun & Informat Engn Xian 710121 Peoples R China Xian Univ Posts & Telecommun Ctr Image & Informat Proc Xian 710121 Peoples R China

image processing is a continuously evolving field, and due to the fact that color images contain more information than grayscale images and are the most encountered type of images in daily life, the analysis and processing of color images are particularly important. The use of the H component of the HSI color space model to construct a circular histogram for selecting the optimal threshold is one method in color image segmentation. However, traditional circular histograms only reflect the pixel distribution of the entire image by statistically counting the frequency of pixel occurrences in the entire image, without considering the spatial context information of the image for optimal threshold selection. To address this drawback, we propose the method of circular energy curves (CEC). This method involves calculating energy curves on the H component to construct CEC. The optimal threshold is then selected based on these curves. To reduce the complexity of threshold selection for CEC and enhance their generality, we combine cumulative distribution functions with information energy, introducing the method of cumulative distribution information energy (CDF-IE) to determine the optimal breakpoint. After linearizing the circular energy curves (L-CEC), we use the Tsallis entropy thresholding method to achieve the optimal segmentation threshold selection for the H component CEC. To improve the effectiveness of the algorithm, we present the Tsallis entropy recursive algorithm after L-CEC, which reduces the time complexity. The proposed method in this paper, compared to the circular histogram method, achieves better image segmentation results and exhibits stronger applicability.

关键词： color image segmentation HSI color model Circular energy curve Cumulative distribution function Information energy Tsallis entropy

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color image segmentation based on improved sine cosine optimization algorithm

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SOFT COMPUTING 2022年第23期26卷 13193-13203页

作者： Mookiah, Sivasubramanian Parasuraman, Kumar Chandar, S. Kumar JP Coll Arts & Sci Dept Comp Sci Tenkasi India Manonmaniam Sundaranar Univ Ctr Informat Technol & Engn Tirunelveli 627012 Tamil Nadu India CHRIST Deemed Univ Sch Business & Management Bangalore 560029 Karnataka India

segmentation refers to the process of dividing an image into multiple regions based on some criteria such as intensity and color. In recent years, color image segmentation has received considerable attention from the researchers. However, it is still a highly complicated task due to the presence of more attributes or components as compared to monochrome images. Numerous meta-heuristics algorithms are developed to determine the optimal threshold value for segmenting color images efficiently. This paper presents an enhanced sine cosine algorithm (ESCA) to seek threshold for segmenting color images. Sine cosine algorithm (SCA) is a population-based optimization algorithm which has the ability of preventing local minima problem. First an input image is transformed to CIE L*a*b* color reduced space. ESCA is applied to determine the optimal threshold values for segmentation. The performance of the proposed method is tested on color images from Berkeley database, and segmentation results are compared with two metaheuristic algorithms, namely particle swarm optimization (PSO) and standard SCA. Experimental results are validated by measuring peak signal-noise ratio (PSNR), structural similarity index and computation time for all the images investigated. Results revealed that the proposed method outperforms the other methods like PSO and SCA by achieving PSNR of 23 dB and SSIM of 0.93 and also require less time for finding optimal threshold values than PSO and SCA.

关键词： color image segmentation Enhanced sine cosine algorithm Metaheuristics algorithm Particle swarm optimization PSNR

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Joint learning framework of superpixel generation and fuzzy sparse subspace clustering for color image segmentation

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SIGNAL PROCESSING 2024年 222卷

作者： Wu, Chengmao Zhao, Jingtian Xian Univ Posts & Telecommun Sch Elect Engn Xian 710121 Peoples R China

Sparse subspace clustering (SSC) is an important image segmentation method that constructs a self- representation coefficient matrix to represent the relationships between pixels, and then uses spectral clustering to achieve clustering. Compared with other unsupervised segmentation algorithms, SSC has good segmentation performance. However, SSC has a high computational complexity when processing large-sized image. To improve computational efficiency, many researchers have proposed superpixel-based SSC algorithms, which process superpixels instead of pixels and improve efficiency through preprocessing. Due to the sensitivity of superpixel generation to noise, superpixel-based SSC algorithms still have poor robustness. Additionally, preprocessing increases the complexity of the algorithm. To address these issues, this paper proposes a robust superpixel-based fuzzy sparse subspace clustering algorithm. This algorithm combines fuzzy sparse subspace clustering with superpixel generation, and it constructs a unified optimization learning framework through fuzzy C-multiple-means clustering to improve segmentation performance and reduce complexity. Additionally, this paper introduces additional features of superpixel in sparse subspace clustering to further enhance the segmentation performance of the algorithm. Experiment results indicate that the proposed algorithm not only outperforms existing state-of-the-art robust segmentation algorithms independent of superpixels, but also is superior to the latest superpixel-based segmentation algorithms.

关键词： color image segmentation Sparse subspace clustering Fuzzy C-multiple-means clustering Superpixel

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Improved artificial rabbits algorithm for global optimization and multi-level thresholding color image segmentation

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ARTIFICIAL INTELLIGENCE REVIEW 2024年第2期58卷 1-57页

作者： Jia, Heming Su, Yuanyuan Rao, Honghua Liang, Muzi Abualigah, Laith Liu, Chibiao Chen, Xiaoguo Sanming Univ Sch Informat Engn Sanming 365004 Peoples R China Sanming Univ Fujian Key Lab Agr IOT Applicat Sanming 365004 Peoples R China Northeast Petr Univ Sch Elect & Informat Engn Daqing 163318 Peoples R China Al Al Bayt Univ Comp Sci Dept Mafrag 25113 Jordan

The Artificial Rabbits Optimization Algorithm is a metaheuristic optimization algorithm proposed in 2022. This algorithm has weak local search ability, which can easily lead to the algorithm falling into local optimal solutions. To overcome these limitations, this paper introduces an Improved Artificial Rabbits Optimization Algorithm (IARO) and demonstrates its effectiveness in multi-level threshold color image segmentation using the Otsu method. Initially, we apply a center-driven strategy to enhance exploration by updating the rabbit's position during the random hiding phase. Additionally, when the algorithm stalls, a Gaussian Randomized Wandering (GRW) strategy is utilized to enable the algorithm to escape local optima and improve convergence accuracy. The performance of the IARO algorithm is evaluated using 23 standard benchmark functions and CEC2020 benchmark functions, and compared with nine other algorithms. Experimental results indicate that IARO excels in global optimization and demonstrates notable robustness. To assess its effectiveness in multi-threshold color image segmentation, the algorithm is tested on classical Berkeley images. Evaluation metrics including execution time, Peak Signal-to-Noise Ratio (PSNR), Feature Similarity (FSIM), Structural Similarity (SSIM), Boundary Displacement Error (BDE), The Probabilistic Rand Index (PRI), Variation of Information (VOI) and average fitness value are used to measure segmentation quality. The results reveal that IARO achieves high accuracy and fast segmentation speed, validating its efficiency and practical utility in real-world applications.

关键词： Artificial rabbits optimization Otsu method color image segmentation Gaussian random walk 23 standard benchmark functions CEC2020 benchmark functions

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Giza pyramids construction algorithm with gradient contour approach for multilevel thresholding color image segmentation

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APPLIED INTELLIGENCE 2023年第18期53卷 21248-21267页

作者： Wu, Bowen Zhu, Liangkuan Li, Xin Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Luo Yu Rd Wuhan 430074 Peoples R China Northeast Forestry Univ Sch Mech & Elect Engn He Xing Rd Harbin 15004 Peoples R China

segmentation is an essential processing step in computer vision. As a popular method in image segmentation, the pivotal of multilevel thresholding lies in determining specific thresholds of the image. In this paper, we introduce an enhanced Giza pyramids construction algorithm (GPC) with a gradient contour approach, termed GGPC, for solving color image segmentation problems. In the proposed GGPC algorithm, the gradient contour approach explores the fitness landscape based on population distributions to speculate promising summits, and provides an efficient guidance for further evolution. A composite suit of benchmark functions is adopted to evaluate the proposed GGPC algorithm which is shown via extensive comparisons to outperform eight state-of-the-art algorithms. To further develop the application potential, we propose an excellent GGPC-based thresholding segmentation method by multilevel Kapur entropy. The method is successfully exploited in color image segmentation with respect to different categories of benchmark images. Experiment results demonstrate that the GGPC-based segmentation method also exhibits better performance over peers, providing a more effective technique for color image segmentation.

关键词： Heuristic algorithm color image segmentation Giza pyramids construction algorithm Kapur entropy Gradient contour approach

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An improved mayfly algorithm based on Kapur entropy for multilevel thresholding color image segmentation

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第1期44卷 365-380页

作者： Zhao, Xiaohan Zhu, Liangkuan Wu, Bowen Northeast Forestry Univ Coll Mech & Elect Engn Harbin Heilongjiang Peoples R China Suihua Univ Sch Elect Engn Suihua Heilongjiang Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan Peoples R China

Multilevel thresholding segmentation of color images plays an important role in many fields. The pivotal procedure of this technique is determining the specific threshold of the images. In this paper, an improved mayfly algorithm (IMA)-based color image segmentation method is proposed. Tent mapping initializes the female mayfly population to increase population diversity. Levy flight is introduced in the wedding dance iterative formulation to make IMA jump from the local optimal solution quickly. Two nonlinear coefficients were designed to speed up the convergence of the algorithm. To better verify the effectiveness, eight benchmark functions are used to test the performance of IMA. The average fitness value, standard deviation, and Wilcoxon rank sum test are used as evaluation metrics. The results show that IMA outperforms the comparison algorithm in terms of search accuracy. Furthermore, Kapur entropy is used as the fitness function of IMA to determine the segmentation threshold. 10 Berkeley images are segmented. The best fitness value, peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and other indexes are used to evaluate the effect of segmented images. The results show that the IMA segmentation method improves the segmentation accuracy of color images and obtains higher quality segmented images.

关键词： Non-linear attraction coefficients Tent chaotic mapping Levy flight color image segmentation mayfly algorithm

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A Novel Radial Kernel Watershed Basis segmentation Algorithm for color image segmentation

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WIRELESS PERSONAL COMMUNICATIONS 2023年第4期133卷 2105-2124页

作者： Kumari, Chandana Mustafi, Abhijit Birla Inst Technol Dept Comp Sci & Engn Mesra Ranchi India

Human vision cannot analyze the huge amount of information hidden in color images. Thus, amongst the research being developed currently, color image segmentation (CIS) has become more vital. As each pixel in the image constitutes a different color complexion, which is tedious for estimating, CIS is a challenging process;also, it is a time-consuming process. Hence, this work proposes novel thresholding utilizing the Chaotic Logistic Fertile Field optimization Algorithm (CLFFA) approach and segmentation utilizing the Radial Kernel Watershed Basis segmentation (RKWBS) technique for reducing time consumption. Utilizing the Contrast Limited Recursive Least Square Histogram Equalization (CLRLSHE) algorithm, the input image's contrast is enriched initially. Then, the Sobel edge detection detects the edges from the preprocessed image. Also, the CLFFA algorithm finds the optimal threshold value. After that, by thresholding the edge detected image grounded on RKWBS, the image is segmented quickly. Lastly, for removing the unwanted information in the segmented image, morphological operations are executed in it. Experiments were executed and analogized with the prevailing frameworks for proving the proposed mechanism's efficacy. The outcomes proved the efficacy regarding segmentation accuracy, computational time, et cetera.

关键词： Contrast Limited Recursive Least Square Histogram Equalization (CLRLSHE) Chaotic Logistics Fertile Field Algorithm (CLFFA) Radial Kernel Watershed Basis segmentation (RKWBS) color image segmentation Sobel edge detection

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color image segmentation using Fuzzy Histon 7

Color Image Segmentation using Fuzzy Histon

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7th IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering (WIECON-ECE)

作者： Mushrif, Milind M. Dubey, Yogita Gupta, Vikas Yeshwantrao Chavan Coll Engn Dept Elect & Telecommun Engn Nagpur Maharashtra India Shri Ramdeobaba Coll Engn & Management Dept Elect Engn Nagpur Maharashtra India

ISBN: (纸本)9781665478496

Fuzzy histon, an extension of histon, is a contour plotted on the top of the histograms of the color components such that the pixels with high degree of correlation are visualized as belonging to one singular value. In this paper, we propose a fuzzy histon roughness index based thresholding algorithm for the segmentation of color images. The quantitative evaluation of the proposed approach against the existing thresholding technique and Compression-based Texture Merging (CTM) algorithm proves its effectiveness.

关键词： histon color image segmentation roughness index

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CTMFSO algorithm-based efficient color image segmentation by fuzzy order entropy

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MULTIMEDIA TOOLS AND APPLICATIONS 2022年 1-14页

作者： Kumari, Chandana Mustafi, Abhijit Birla Inst Technol Dept Comp Sci & Engn Mesra India

Identifying the object on the image or identifying disparate objects or regions independently is the major goal of image Analysis (IA). image segmentation (IS) is implemented extensively in image Processing (IP) as well as object recognition. In the instance of color images, segmentation is more intricate. To find the optimum Threshold Values (TV) aimed at segmenting an image, disparate techniques are used. The technique encompasses poor PSNR together with higher Execution Time (ET). This research method proposed Chaotic Tent Map function-based Sailfish Optimization (CTMFSO) algorithm-centered color IS by means of considering the proposed Fuzzy Order Entropy (FOE) as the Objective Function (OF) for solving that issue. The proposed work sets the "OF' via FOE. Next, the proposed CTMFSO selects the optimal TV. After that, the image is segmented via the chosen TV. The CTMFSO's performance is compared with the conventional research algorithms rounded on the Structural Similarity Index (SSIM), Peak signal to noises ratio (PSNR), ET, together with fitness vs iteration. The proposed CTMFSO centered color IS attains better performance compared to the prevailing algorithms. Therefore, it proved the proposed work's performance.

关键词： Objective function color image segmentation Optimal threshold selection Fuzzy order entropy (FOE) Chaotic tent map function-based sailfish optimization (CTMFSO)

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IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI)

作者： Gherbaoui, Radhwane Benamrane, Nacera Ouali, Mohammed Univ Sci & Technol Oran Lab SIMPA Dept Informat Oran Algeria Adrian Coll Dept Comp Sci Adrian MI USA

ISBN: (纸本)9798350334586

Cluster analysis is an important task in data analysis and machine learning. Traditional clustering methods, such as partitioning and density-based approaches, have limitations in identifying natural clusters in datasets with elliptical and chained shapes. In this paper, we propose a novel hierarchical clustering algorithm for color image segmentation that addresses these limitations by quantifying the degree of overlap between clusters as a similarity measure for the merging process.

关键词： merging approach color image segmentation hierarchic method k-means

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