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A mean shift algorithm incorporating reachable distance for spatial clustering

INFORMATION SCIENCES 2025年 689卷

作者： Peng, Youwei Luo, Yalan Zhang, Qiongbing Xie, Chengwang Hunan Univ Sci & Technol Taoyuan Rd Xiangtan 411201 Hunan Peoples R China South China Normal Univ Magong St Shanwei 516600 Guangdong Peoples R China

Spatial clustering is a widely used technique in spatial analysis that groups similar objects together based on their proximity in space. However, traditional clustering algorithms often fail to ensure the accessibility of cluster centers, which limits their validity in practical applications such as facility location problems. To address this issue, this article introduces a novel mean shift algorithm that incorporates reachable distance and an iterative mechanism to accurately locate cluster centers. The proposed algorithm initially labels clustering elements with road network coordinates to facilitate the calculation of reachable distance and the cluster center iterative mechanism. Subsequently, the mean shift vector function is modified to employ reachable distance as the measure of geographic reachable similarity. Unlike existing algorithms, our approach allows for cluster centers to be positioned independently of the clustering elements, guaranteeing geographical accessibility. Through simulation experiments, we demonstrate that our proposed algorithm not only outperforms existing methods in terms of solution quality, but also effectively addresses the limitations of disregarding geographical obstacles and unreachable cluster centers.

关键词： Spatial clustering mean shift algorithm Geographical accessibility Cluster center iteration mechanism

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mean shift algorithm and its application in tracking of objects

Mean shift algorithm and its application in tracking of obje...

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5th International Conference on Machine Learning and Cybernetics

作者： Wen, Zhi-Qiang Cai, Zi-Xing Cent South Univ Coll Informat Sci & Engn Changsha 410083 Peoples R China

ISBN: (纸本)1424400600

mean shift algorithm is recently widely used in tracking clustering, etc, however convergence of mean shift algorithm has not been rigorously proved. In this paper mean shift algorithm with Gaussian profile is studied and applied to tracking of objects. The imprecise proofs about convergence of mean shift are firstly pointed out. Then a convergence theorem and its rigorous convergence proof are provided. Lastly tracking approach of objects based on mean shift is modified. The results of experiment show the modified approach has good performance of object tracking applied to occlusion. The contributions in this paper are expected to further study and application in mean shift algorithm.

关键词： mean shift algorithm convergence kernel function tracking of object Bhattacharyya coefficient

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DEVELOPMENT OF A PATH PLANNING SYSTEM USING mean shift algorithm FOR DRIVER ASSISTANCE

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INTERNATIONAL JOURNAL OF AUTOMOTIVE TECHNOLOGY 2011年第1期12卷 119-124页

作者： Hwang, J. Lee, D. Huh, K. Na, H. Kang, H. Hanyang Univ Dept Automat Engn Grad Sch Seoul 133791 South Korea Mando Corp Cent R&D Ctr Yongin 449901 Gyeonggi South Korea

The longitudinal and lateral vehicle control techniques have been widely used in several active driver assistance systems. The adaptive cruise control, lane keeping assistant control, vehicle platooning and stop-and-go control are typical examples of the most important applications. In this study, a novel path planning method is proposed considering the driving environment such as road shape, ego vehicle and surrounding vehicles' movement. The relative distance and velocity between the ego vehicle and surrounding vehicles are identified with respect to the predicted lane shape in front of the ego vehicle. Based on the identified information, the road shape and surrounding vehicles are mapped into the intensity image and the desired vector for the ego vehicle's movement is determined by the maximum intensity density tracing method. The desired vehicle path is followed by the acceleration/deceleration control and the steering assist control, respectively. In order to evaluate the performance of the proposed system, simulations are conducted and compared with ACC systems.

关键词： Local path planning Lane keeping assistance system Adaptive cruise control mean shift algorithm Surrounding vehicle mapping

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The mean shift algorithm and its relation to kernel regression

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INFORMATION SCIENCES 2016年 348卷 198-208页

作者： Ghassabeh, Youness Aliyari Rudzicz, Frank Toronto Rehabil Inst UHN 550 Univ Ave Toronto ON Canada Univ Toronto Dept Comp Sci Toronto ON Canada

We investigate the connection between the asymptotic bias of the well-known Nadaraya-Watson kernel regression and the mean shift (MS) vector with the Gaussian kernel. We first show that the asymptotic bias for the univariate Nadaraya-Watson kernel regression can be estimated using the MS scalar. Then, we investigate the general D-dimensional case (D > 1) and derive a formula for the asymptotic bias as a function of the MS vector. We show that when the regression function is a linear function of its entries, then the asymptotic bias can be represented as a linear function of the MS vector. The MS algorithm for the univariate and the linear cases can be used to find points where the Nadaraya-Watson kernel regression gives an unbiased estimate. Furthermore, this connection suggests that exploiting the theoretical properties of the asymptotic bias of the Nadaraya-Watson kernel regression may be helpful to show the convergence of the MS algorithm. Through the simulations, we show that how the given theoretical results can be used to estimate the bias of the estimated clean signal by just observing the noisy signal. Having access to the bias of the estimator, enables us to evaluate the accuracy of the estimated clean data, which later will be used to design a quantizer. (C) 2016 Elsevier Inc. All rights reserved.

关键词： Nadaraya-Watson kernel regression mean shift algorithm Asymptotic bias Gaussian kernel Noisy source vector quantization

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Modified mean shift algorithm

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IET IMAGE PROCESSING 2018年第12期12卷 2172-2177页

作者： Ghassabeh, Youness Aliyari Rudzicz, Frank Toronto Rehabil Inst UHN 550 Univ Ave Toronto ON Canada Univ Toronto Dept Comp Sci Toronto ON Canada

The mean shift (MS) algorithm is an iterative method introduced for locating modes of a probability density function. Although the MS algorithm has been widely used in many applications, the convergence of the algorithm has not yet been proven. In this study, the authors modify the MS algorithm in order to guarantee its convergence. The authors prove that the generated sequence using the proposed modified algorithm is a convergent sequence and the density estimate values along the generated sequence are monotonically increasing and convergent. In contrast to the MS algorithm, the proposed modified version does not require setting a stopping criterion a priori;instead, it guarantees the convergence after a finite number of iterations. The proposed modified version defines an upper bound for the number of iterations which is missing in the MS algorithm. The authors also present the matrix form of the proposed algorithm and show that, in contrast to the MS algorithm, the weight matrix is required to be computed once in the first iteration. The performance of the proposed modified version is compared with the MS algorithm and it was shown through the simulations that the proposed version can be used successfully to estimate cluster centres.

关键词： iterative methods probability matrix algebra convergence of numerical methods kernel density estimate original MS algorithm generated sequence modified MS algorithm convergent sequence density estimate values mean shift algorithm nonparametric iterative method probability density function machine learning upper bound weight matrix

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Generalized Transport mean shift algorithm for ubiquitous intelligence

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SIMULATION-TRANSACTIONS OF THE SOCIETY FOR MODELING AND SIMULATION INTERNATIONAL 2012年第10期88卷 1202-1215页

作者： Sunat, Khamron Padungweang, Panida Chiewchanwattana, Sirapat Khon Kaen Univ Dept Comp Sci Fac Sci Khon Kaen 40002 Thailand

Much research has been conducted recently relating to ubiquitous intelligent computing. Ubiquitous intelligence-enabled techniques, such as clustering and image segmentation, have focused on the development of intelligence methodologies. In this paper, a simultaneous mode-seeking and clustering algorithm called the Generalized Transport mean shift (GTMS) was introduced. The data points were designated as the 'transporter-trailer' characteristic. The important concept of transportation was used to solve the problem of redundant computations of mode-seeking algorithms. The time complexity of the GTMS algorithm is much lower than that of the mean shift (MS) algorithm. This means it is able to be used in a problem that has a very high data point, in particular, the segmentation of images containing the green vegetation. The proposed algorithm was tested on clustering and image-segmentation problems. The experimental results showed that the GTMS algorithm improves upon the existing algorithms in terms of both accuracy and time consumption. The GTMS algorithm's highest speed is also 333.98 times faster than that of the standard MS algorithm. The redundancy computation can be reduced by omitting more than 90% of the data points at the third iteration of the mode-seeking process. This is because GTMS algorithm mainly reduces the data in the mode-seeking process. Thus, use of the GTMS algorithm would allow for the building of an intelligent portable device for surveying green vegetables in a ubiquitous environment.

关键词： mean shift algorithm agglomerative mean shift clustering Generalized Transport mean shift algorithm image segmentation clustering mode seeking ubiquitous intelligence

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Exudates Detection Using Morphology mean shift algorithm in Retinal Images

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IEEE ACCESS 2019年 7卷 11946-11958页

作者： Wisaeng, Kittipol Sa-Ngiamvibool, Worawat Mahasarakham Univ Mahasarakham Business Sch Technol & Business Informat Syst Unit Maha Sarakham 44150 Thailand Mahasarakham Univ Fac Engn Dept Elect & Comp Engn Maha Sarakham 44150 Thailand

Exudates are a serious complication causing blindness in diabetic retinopathy patients. The main objective of this paper is to develop a novel method to detect exudates lesions in color retinal images by using a morphology mean shift algorithm. The proposed method start with a normalization of the retinal image, contrast enhancement, noise removal, and the localization of the OD. Then, a coarse segmentation method by using mean shift provides a set of exudates and non-exudates candidates. Finally, a classification using the mathematical morphology algorithm (MMA) procedure is applied in order to keep only exudates pixels. The optimal value parameters of the MMA will facilitate an increase of the accuracy results from the solely MSA method by 13.10%. Based on a comparison between the results and ground truth images, the proposed method obtained an average sensitivity, specificity, and accuracy of detecting exudates as 98.40%, 98.13%, and 98.35%, respectively.

关键词： Diabetic retinopathy retinal image exudates mean shift algorithm mathematical morphology

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A method of security management of early warning based on mean shift algorithm and data mining

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020年第6期39卷 8953-8960页

作者： Wang, Jin Sch Police Adm Railway Police Coll 31 Nong Ye Rd Zhengzhou Henan Peoples R China

Facing COVID-19 epidemic, many countries have recently strengthened epidemic prevention and control measures. The reliability of safety management is of great significance to personnel management and control during the COVID-19 epidemic period. The focus of security management of early warning is to monitor and identify the moving target. The current optical flow method is vulnerable to the influence of light changes and background movement, and it is not very accurate for moving target detection in dynamic complex background. In this paper, aiming at the traditional Lucas Kanade optical flow method, the inter frame difference method, mean shift clustering algorithm and morphological processing are combined to optimize and improve on the original basis, so that the moving target detection effect in both simple and complex environments is significantly improved. At the same time, the improved algorithm also reduces the execution time to a certain extent, and has a certain resistance to noise interference such as light changes. This has a certain ability test value for personnel control during the epidemic.

关键词： Lucas Kanade optical flow method COVID-19 mean shift algorithm security management moving target

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Real-time color image segmentation based on mean shift algorithm using an FPGA

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JOURNAL OF REAL-TIME IMAGE PROCESSING 2015年第2期10卷 345-356页

作者： Trieu, Dang Ba Khac Maruyama, Tsutomu Univ Tsukuba Syst & Informat Engn Tsukuba Ibaraki 3058573 Japan

Image segmentation is one of the most important tasks in the image processing, and mean shift algorithm is often used for color image segmentation because of its high quality. The computational cost of the mean shift algorithm, however, is high, and it is difficult to realize its real time processing on microprocessors, though many techniques for reducing the cost have been researched. In this paper, we describe an FPGA system for the image segmentation based on the mean shift algorithm. In the image segmentation based on the mean shift algorithm, the image is once over-segmented, and then the small regions are merged considering the similarity between the over-segmented regions to obtain better segmentation. In our system, the mean shift filter is accelerated using a cache memory which can access to all pixels in a w (s) x w (s) pixel window at arbitrary position. This cache memory allows us to process w (s) x w (s) pixels in parallel every clock cycle. The region merging is also accelerated by not strictly managing the list structures used for the merging. This loose management causes the redundant and out-of-date data into the list structures, but it makes the pointer dereferences unnecessary, and the overhead by those data can be hidden by pipeline processing. The performance for 768 x 512 pixel images is fast enough for real-time applications.

关键词： mean shift algorithm Segmentation Region merging real time FPGA

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Asymptotic stability of equilibrium points of mean shift algorithm

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MACHINE LEARNING 2015年第3期98卷 359-368页

作者： Ghassabeh, Youness Aliyari Queens Univ Dept Math & Stat Kingston ON K7L 3N6 Canada

The mean shift (MS) algorithm is a popular non-parametric technique that has been widely used in statistical pattern recognition and machine learning. The algorithm iteratively tries to find modes of an estimated probability density function. These modes play an important role in many applications, such as clustering, image segmentation, feature extraction, and object tracking. The modes are fixed points of a discrete, nonlinear dynamical system. Although the algorithm has been successfully used in many applications, a theoretical study of its convergence is still missing in the literature. In this paper, we first consider the iteration index as a continuous variable and, by introducing a Lyapunov function, show that the equilibrium points are asymptotically stable. We also show that the proposed function can be considered as a Lyapunov function for the discrete case with the isolated stationary points. The availability of a Lyapunov function for continuous and discrete cases shows that if the MS iterations start in a neighborhood of an equilibrium point, the generated sequence remains close to that equilibrium point and finally converges to it.

关键词： mean shift algorithm Lyapunov function Mode estimate sequence Asymptotically stable Convex function Equilibrium point Fixed point

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