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fuzzy c-means clustering with Region constraints for Superpixel Generation

INTERNATIONAL JOURNAL OF fuzzy SYSTEMS 2025年 1-19页

作者： Jia, Xiaohong Zhao, Yao Zhang, Bin Zhang, Xuejun Yan, Guanghui Lanzhou Jiaotong Univ Sch Elect & Informat Engn Lanzhou 730070 Gansu Peoples R China Beijing Jiaotong Univ Key Lab Big Data & Artificial Intelligence Transpo Minist Educ Beijing 100044 Peoples R China Beijing Jiaotong Univ Inst Informat Sci Beijing 100091 Peoples R China

Superpixels are becoming increasingly essential in computer vision applications, as they significantly reduce the number of computational primitives required for subsequent tasks. However, most existing superpixel algorithms primarily focus on color intensity and position coordinates, often ignoring local neighborhood factors and region constraints. This oversight reduces their effectiveness in noisy and cluttered environments. To address this issue, we propose a seminal and novel fuzzy c-means clustering with Region constraints for Superpixel generation (RcFcMS). First, RcFcMS employs watershed transform to establish region constraints, effectively preventing boundary crossings. Second, relative position relationship is utilized to initialize superpixel centers within each region, offering an adaptive grid layout. Third, RcFcMS adopts spatial information to perceive local similarity, significantly mitigating noise interference. Finally, connected components are applied to eliminate isolated pixels, further optimizing pixel assignment. The experimental results on two benchmark datasets demonstrate that the proposed RcFcMS achieves competitive performance across varying numbers of superpixels. Specifically, when the number of superpixels is 500, RcFcMS achieves the following metrics: UE = 0.102, ASA = 0.948, FM = 0.401, and cO = 0.572 on BSDS500;and UE = 0.124, ASA = 0.935, FM = 0.296, and cO = 0.608 on SBD. These results further highlight the effectiveness of RcFcMS for superpixel generation.

关键词： fuzzy c-means clustering Superpixel generation Region constraints Noise interference

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Power short-term load forecasting based on fuzzy c-means clustering and improved locally weighted linear regression

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TRANSAcTIONS OF THE INSTITUTE OF MEASUREMENT AND cONTROL 2025年第2期47卷 278-290页

作者： Niu, Shuqi Zhang, Zhao Zhou, Hongyan chen, Xue-Bo Univ Sci & Technol Liaoning Sch Comp Sci & Software Engn Anshan 114051 Peoples R China Univ Sci & Technol Liaoning Sch Elect & Informat Engn Anshan Peoples R China

Power load forecasting is an important part of modern smart grid operation management. Accurate forecasting guides the efficient and stable operation of the power system. In this paper, a fuzzy c-means clustering algorithm and an improved locally weighted linear regression model are proposed for short-term power load forecasting. First, the fuzzy c-means clustering algorithm is used to cluster the power load. Make the power consumption behavior of load data of the same category similar and use the power consumption load data of the same category as the training sample. Then, to solve the problem of large calculation and insufficient fitting of the locally weighted linear regression model, the k-nearest neighbor range constraint is introduced into the model for daily load forecasting. Finally, the effectiveness of the method is verified by a simulation example. Experimental results show that this method can effectively improve the accuracy and generalization ability of power load forecasting compared with other methods.

关键词： Short-term load forecasting fuzzy c-means clustering k-nearest neighbor locally weighted linear regression smart grid

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Wasserstein distance-based fuzzy c-means clustering in Riemannian manifold feature space for image segmentation

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SIGNAL IMAGE AND VIDEO PROcESSING 2025年第1期19卷 1-14页

作者： Wu, chengmao Zheng, Jia Xian Univ Posts & Telecommun Sch Elect Engn Xian 710121 Peoples R China

To ensure the accuracy of image segmentation, selecting appropriate image features is crucial. The existing image segmentation methods mainly utilize spectral features of images to achieve image segmentation, which have low segmentation accuracy and weak robustness, and are difficult to adapt to the needs of complex image segmentation. Therefore, this paper proposes a new robust fuzzy clustering image segmentation based on Wasserstein distance in the Riemannian manifold feature space. At first, the spectral features of pixels in the neighborhood window around the current pixel are constructed into a Gaussian normal distribution structure model, and the original image is mapped to the Riemannian manifold feature space to achieve Riemannian manifold feature modeling of image feature information. Secondly, the Wasserstein distance is used to measure the difference between two Gaussian Riemannian manifolds, and a robust fuzzy clustering method for image segmentation is proposed in Riemannian manifold feature space. Finally, the local convergence of the algorithm is proved using the Zangwill theorem and bordered Hessian matrix. The experimental results demonstrate that the proposed algorithm has good segmentation performance and strong noise resistance. compared with existing segmentation algorithms based on spectral feature space and Riemannian manifold feature space, this proposed algorithm is more effective and robust in segmenting images with or without noise.

关键词： Image segmentation Riemannian manifold Gaussian distribution fuzzy c-means clustering Wasserstein distance Robustness

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Road crack detection using pixel classification and intensity-based distinctive fuzzy c-means clustering

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VISUAL cOMPUTER 2025年第3期41卷 1689-1704页

作者： Bhardwaj, Munish Khan, Nafis Uddin Baghel, Vikas Jaypee Univ Informat Technol Solan India SR Univ Warangal India

Road cracks are quickly becoming one of the world's most serious concerns. It may have an impact on traffic safety and increase the likelihood of road accidents. A significant amount of money is spent each year for road repair and upkeep. This cost can be lowered if the cracks are discovered in good time. However, detection takes longer and is less precise when done manually. Because of ambient noise, intensity in-homogeneity, and low contrast, crack identification is a complex technique for automatic processes. As a result, several techniques have been developed in the past to pinpoint the specific site of the crack. In this research, a novel fuzzy c-means clustering algorithm is proposed that will detect fractures automatically by adding optimal edge pixels utilizing a second-order difference and intensity-based edge and non-edge fuzzy factors. This technique provides information of the intensity of edge and non-edge pixels, allowing it to recognize edges even when the image has little contrast. This method does not necessitate the use of any data set to train the model and no any critical parameter optimization is required. As a result, it can recognize edges or fissures even in novel or previously unknown input pictures of different environments. The experimental results reveal that the unique fuzzy c-means clustering-based segmentation method beats many of the existing methods used for detecting alligator, transverse, and longitudinal fractures from road photos in terms of precession, recall, and F1 score, PSNR, and execution time.

关键词： K-means clustering fuzzy c-means clustering Road crack detection

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A fuzzy c-means clustering approach for petrophysical characterization of lithounits in the North Singhbhum Mobile Belt, Eastern India

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AcTA GEOPHYSIcA 2025年第1期73卷 439-455页

作者： Arasada, Rama chandrudu Kumar, Santosh Rao, Gangumalla Srinivasa Biswas, Anirban Sahoo, Prabodha Ranjan Singh, Sahendra Indian Inst Technol Dept Appl Geophys Indian Sch Mines Dhanbad Dhanbad 826004 Jharkhand India Indian Inst Technol Dept Earth Sci Mumbai 400076 India Indian Inst Technol Dept Appl Geol Indian Sch Mines Dhanbad Dhanbad 826004 Jharkhand India

The characterization of the various rock types through petrophysical data analysis is essential for comprehending geological processes and enhancing the efficacy of geophysical approaches aimed at mineralization zones. In the present study, a fuzzy c-means (FcM) clustering algorithm was employed to automatically classify lithounits within the western sector of the North Singhbhum Mobile Belt based on the petrophysical properties. Laboratory measurements of 326 rock samples from the study area show a wide range of density (similar to 2350-3150 kg/m(3)) and magnetic susceptibility (10(-5) SI to 10(-1) SI) values. Further FcM analysis reveals three distinct clusters: (i) cluster 1 displays high density and low magnetic susceptibility responses and comprises majorly metabasic, phyllite, and mica schist rocks, (ii) cluster 2 shows low density and low magnetic susceptibility characteristics and contains mainly metasedimentary rocks (phyllite, quartzite, and mica schist) and (iii) cluster 3 also primarily encompasses metasedimentary rocks, but it displays the low density and high magnetic susceptibility characteristics. Overlap of rock types in different clusters probably indicates the influence of secondary geological processes on the petrophysical measurements such as metamorphism, alteration, and weathering, which is also supported by the petrographical studies. Overall, the present study demonstrates the potential utility of the FcM algorithm for automatic lithology classification and inferring the associated geological processes from the petrophysical measurements. Furthermore, the correlation between the geophysical and petrophysical clusters highlights the role of petrophysical information in the automatic geological/mineral mapping. However, the complexity in cluster attributes on a detailed scale suggests that future studies in the NSMB should focus on comprehensive multi-parameter petrophysical and geochemical measurements. This approach will help in developing b

关键词： North Singhbhum mobile belt Petrophysical data fuzzy c-means clustering Automatic lithology classification

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Grouped machine learning methods for predicting rock mass parameters in a tunnel boring machine-driven tunnel based on fuzzy c-means clustering

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Deep Underground Science and Engineering 2025年第1期4卷 55-71页

作者： Ruirui Wang Yaodong Ni Lingli Zhang Boyang Gao The School of Civil Engineering Shandong Jianzhu UniversityJinanChina Northeastern University Khoury College of Computer Sciences BostonMassachusettsUSA

To guarantee safe and efficient tunneling of a tunnel boring machine(TBM),rapid and accurate judgment of the rock mass condition is *** on fuzzy c-means clustering,this paper proposes a grouped machine learning method for predicting rock mass *** elaborate data set on field rock mass is collected,which also matches field TBM ***,target stratum samples are divided into several clusters by fuzzy c-means clustering,and multiple submodels are trained by samples in different clusters with the input of pretreated TBM tunneling data and the output of rock mass parameter *** testing sample or newly encountered tunneling condition can be predicted by multiple submodels with the weight of the membership degree of the sample to each *** proposed method has been realized by 100 training samples and verified by 30 testing samples collected from the c1 part of the Pearl Delta water resources allocation *** average percentage error of uniaxial compressive strength and joint frequency(Jf)of the 30 testing samples predicted by the pure back propagation(BP)neural network is 13.62%and 12.38%,while that predicted by the BP neural network combined with fuzzy c-means is 7.66%and6.40%,*** addition,by combining fuzzy c-means clustering,the prediction accuracies of support vector regression and random forest are also improved to different degrees,which demonstrates that fuzzy c-means clustering is helpful for improving the prediction accuracy of machine learning and thus has good ***,the proposed method is valuable for predicting rock mass parameters during TBM tunneling.

关键词： fuzzy c-means clustering machine learning rock mass parameter tunnel boring machine

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Ride-Hailing Service Pattern Recognition and Demand Prediction: A Reinforcement Ensemble Learning With fuzzy c-means clustering Approach

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IEEE TRANSAcTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2025年

作者： Jin, Kun Feng, Ziyan Li, Xiang Zhang, Fengting Beijing Univ Chem Technol Sch Econ & Management Beijing 100029 Peoples R China Beijing Inst Technol Sch Management Beijing 100081 Peoples R China Beijing Jiaotong Univ Sch Traff & Transportat Beijing 100044 Peoples R China

Demand prediction is essential for enhancing the service quality of ride-hailing platforms. However, the imbalanced and highly skewed nature of ride-hailing demand poses challenges in achieving accurate predictions, especially at major transport hubs. Existing studies have predominantly focused on enhancing prediction models or algorithms, while the lack of attention given to understanding the underlying data characteristics inevitably leads to a mediocre prediction performance. To address this issue, this paper presents a practical approach-referred to Pattern Recognition and Prediction-that makes the trade-off between data intricacy and model flexibility. Firstly, the feature-weighted fuzzy c-means clustering algorithm is employed to assign appropriate pattern labels to daily demand order sequences. Subsequently, reinforcement learning assists ensemble learning to recognize the service pattern for new instance and predict the recognized patterns based on the multi-layer stacking model. In particular, the reinforcement learning dynamically identifies the most suitable combination of predictors, which are then efficiently stacked using a multi-layer stacking model. To quantify the uncertainty in predictions, an improved kernel density estimation is additionally developed for interval predictions. Extensive experiments on a real-world ride-hailing dataset from Beijing West Railway Station, china, demonstrate improvements in both point and interval prediction accuracy, with the former exhibiting a minimum increase of 3.37% compared to benchmark models without pattern recognition, and the latter achieving a more balanced interval width and coverage compared to traditional parametric methods.

关键词： Ride-hailing demand prediction fuzzy c-means clustering reinforcement learning ensemble learning interval prediction Ride-hailing demand prediction fuzzy c-means clustering reinforcement learning ensemble learning interval prediction

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fuzzy c-means clustering-based mating restriction for multiobjective optimization

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INTERNATIONAL JOURNAL OF MAcHINE LEARNING AND cYBERNETIcS 2018年第10期9卷 1609-1621页

作者： Zhang, Yi Li, Zimu Zhang, Hu Yu, Zhen Lu, Tongtong China Three Gorges Univ Coll Mech & Power Engn Yi Chang 443002 Peoples R China Beijing Electromech Engn Inst Beijing 100074 Peoples R China China Three Gorges Univ Coll Econ & Management Yi Chang 443002 Peoples R China

Mating restriction is an important approach to improve the performance of multiobjective evolutionary algorithms (MOEAs). This paper designs a fuzzy c-means clustering-based mating restriction (FMR) scheme, and proposes a fuzzy c-means clustering-based MOEA named as FcMMO. FMR employs a fuzzy c-means clustering algorithm to discover the clustering structure of the solutions in the population. Based on the structure, a specific mating pool is determined for each solution to generate new solutions. Experimental studies show that FcMMO outperforms five state of the art MOEAs on a set of test instances with complicated Pareto front shapes and Pareto set structures, and FMR significantly contributes to the good performance of FcMMO.

关键词： Evolutionary algorithm Multiobjective optimization Mating restriction fuzzy c-means clustering

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fuzzy c-means clustering with weighted image patch for image segmentation

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APPLIED SOFT cOMPUTING 2012年第6期12卷 1659-1667页

作者： Ji, Zexuan Xia, Yong chen, Qiang Sun, Quansen Xia, Deshen Feng, David Dagan Nanjing Univ Sci & Technol Sch Comp Sci & Technol Nanjing 210094 Jiangsu Peoples R China Univ Sydney Sch Informat Technol Biomed & Multimedia Informat Technol BMIT Res Grp Sydney NSW 2006 Australia Northwestern Polytech Univ Sch Comp Sci Xian 710072 Peoples R China Shanghai Jiao Tong Univ Med X Res Inst Shanghai 200025 Peoples R China

fuzzy c-means (FcM) clustering has been widely used in image segmentation. However, in spite of its computational efficiency and wide-spread prevalence, the FcM algorithm does not take the spatial information of pixels into consideration, and hence may result in low robustness to noise and less accurate segmentation. In this paper, we propose the weighted image patch-based FcM (WIPFcM) algorithm for image segmentation. In this algorithm, we use image patches to replace pixels in the fuzzy clustering, and construct a weighting scheme to able the pixels in each image patch to have anisotropic weights. Thus, the proposed algorithm incorporates local spatial information embedded in the image into the segmentation process, and hence improve its robustness to noise. We compared the novel algorithm to several state-of-the-art segmentation approaches in synthetic images and clinical brain MR studies. Our results show that the proposed WIPFcM algorithm can effectively overcome the impact of noise and substantially improve the accuracy of image segmentations. (c) 2012 Elsevier B. V. All rights reserved.

关键词： Image segmentation fuzzy c-means clustering Image patch Anisotropic weight

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fuzzy c-means clustering Image Segmentation Algorithm Based on Hidden Markov Model

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MOBILE NETWORKS & APPLIcATIONS 2022年第3期27卷 946-954页

作者： Xu, Ru Yangtze Normal Univ Coll Big Data & Intelligent Engn Chongqing 408100 Peoples R China

Aiming at the poor anti-jamming effect of traditional fuzzy c-means clustering image segmentation method, a fuzzy c-means clustering image segmentation algorithm based on Hidden Markov model is proposed. Hidden Markov model is used to extract the model state, observation state, initial probability, transition probability and visible symbol probability parameters of fuzzy c-means clustering image. According to these parameters, the image texture is measured, and the image mean is removed to eliminate the influence of brightness. Then the image features are extracted by combining the standard deviation. Through the color space and convert it from RGB model to his model. On the basis of removing the image noise, the image is smoothed to achieve image segmentation. Experimental results show that the algorithm has good segmentation effect, smooth segmentation boundary, little influence of image noise and strong robustness.

关键词： Hidden markov model fuzzy c-means clustering Image segmentation color space conversion Smoothing

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