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Distribution free decomposition of multivariate data [chapter]

Dorin Comaniciu, Peter Meer
1998 Lecture Notes in Computer Science  
The number of clusters and the cluster centres are automatically derived by mode seeking with the mean shift procedure on a reduced set of points randomly selected from the data.  ...  Complex clustering examples and applications are discussed, and convergence of the gradient ascent mean shift procedure is demonstrated for arbitrary distribution and cardinality of the data.  ...  Acknowledgements The authors were supported by the NSF under the grant IRI-9530546.  ... 
doi:10.1007/bfb0033284 fatcat:ewoogjgrtnacfekzvrstvc2sea

Distribution Free Decomposition of Multivariate Data

Dorin Comaniciu, Peter Meer
1999 Pattern Analysis and Applications  
The number of clusters and the cluster centres are automatically derived by mode seeking with the mean shift procedure on a reduced set of points randomly selected from the data.  ...  Complex clustering examples and applications are discussed, and convergence of the gradient ascent mean shift procedure is demonstrated for arbitrary distribution and cardinality of the data.  ...  Acknowledgements The authors were supported by the NSF under the grant IRI-9530546.  ... 
doi:10.1007/s100440050011 fatcat:ixjzjjnp2fhrnirex7fjvrm3j4

The functional mean-shift algorithm for mode hunting and clustering in infinite dimensions [article]

Mattia Ciollaro, Christopher Genovese, Jing Lei, Larry Wasserman
2014 arXiv   pre-print
We introduce the functional mean-shift algorithm, an iterative algorithm for estimating the local modes of a surrogate density from functional data.  ...  We show that the algorithm can be used for cluster analysis of functional data. We propose a test based on the bootstrap for the significance of the estimated local modes of the surrogate density.  ...  We can find the local modes of this surrogate density and the corresponding clusters. 3.  ... 
arXiv:1408.1187v1 fatcat:gfd6upq4tje63fm3sqfnbyr27q

Principal graphs and piecewise linear subspace constrained mean-shift

Umut Ozertem, Deniz Erdogmus
2008 2008 IEEE Workshop on Machine Learning for Signal Processing  
One of the important problems with most existing principal curve algorithms is that they are seeking for a smooth curve.  ...  Principal curves have been defined as self-consistent smooth curves that pass through the middle of data.  ...  The authors would like to thank Balazs Kegl for providing the optical character dataset. This work is partially funded by NSF grants ECS-0524835, and ECS-0622239.  ... 
doi:10.1109/mlsp.2008.4685520 fatcat:uogefpktxrekla3375avkzjcsu

A General Method for Unsupervised Segmentation of Images Using a Multiscale Approach [chapter]

Alvin H. Kam, William J. Fitzgerald
2000 Lecture Notes in Computer Science  
is robust through the use of the mean shift clustering and Bayesian multiscale processing.  ...  Clusters in the joint spatio-feature domain are assumed to be properties of underlying classes, the recovery of which is achieved by the use of the mean shift procedure, a robust non-parametric decomposition  ...  On the right: As the mean shift vector is proportional to the density gradient estimate, successive computations of the mean shift define a path leading to a local density maximum (shown here for a 2-D  ... 
doi:10.1007/3-540-45053-x_5 fatcat:hleaeag3dvdqjlkfqaxrsjkax4

An Implementation of the Mean Shift Algorithm

Damir Demirović
2019 Image Processing On Line  
In this paper, we present an implementation and analysis of the mean shift algorithm.  ...  The mean shift is a general non-parametric mode finding/clustering procedure widely used in image processing and analysis and computer vision techniques such as image denoising, image segmentation, motion  ...  The step size of the mean shift is adaptive and depends on the gradient of the density of probability. The gradient is not calculated, instead, a more efficient mean shift vector is calculated.  ... 
doi:10.5201/ipol.2019.255 fatcat:wwbg74ruobcuflcyfm7hkj4gty

Mixture model modal clustering [article]

José E. Chacón
2016 arXiv   pre-print
However, when mixture modeling is used in a nonparametric way, taking advantage of the denseness of the sieve of mixture densities to approximate any density, then the correspondence between clusters and  ...  In the mixture model approach, the density is represented as a mixture and clusters are associated to the different mixture components.  ...  The author acknowledges the support of the Spanish Ministerio de Economía y Competitividad grant MTM2013-44045-P and the Junta de Extremadura grant GR15013.  ... 
arXiv:1609.04721v1 fatcat:ykjdvuwa2zbsbei5zvc3j2iemy

Scale Estimation for Kernel-Based Classification

Nikolaos Nasios, Adrian Bors
2006 Machine Learning for Signal Processing  
The proposed methodology is applied in three different machine learning algorithms: scale space, mean shift and quantum clustering.  ...  The proposed algorithm is applied for the classification of modulated signals and of topography extracted from radar images of terrain.  ...  The mean shift is an iterative algorithm which updates a set of centers based on the local gradient [3, 4] .  ... 
doi:10.1109/mlsp.2006.275551 fatcat:ahbnaqlvybbs5n4tot2ygimxf4

Density-based hardware-oriented classification for spike sorting microsystems

Li-Fang Cheng, Tung-Chien Chen, Nai-Fu Chang, Liang-Gee Chen
2011 2011 5th International IEEE/EMBS Conference on Neural Engineering  
The proposed one is designed based on the density map of the spike features. The density map can be accumulated on-line with the coming of the spike features.  ...  To complete the SSP, a density-based hardware-oriented classification algorithm is proposed for hardware implementation.  ...  That means the label of the cluster is assigned along the highest density gradient as the mean shift algorithm.  ... 
doi:10.1109/ner.2011.5910515 fatcat:hdb3jyr73rfmbhiglf2rp4zflu

Kernel particle filter for visual tracking

Cheng Chang, R. Ansari
2005 IEEE Signal Processing Letters  
Particles are allocated based on the gradient information estimated from the kernel density estimate of the posterior.  ...  The KPF invokes kernels to form a continuous estimate of the posterior density function.  ...  The method is based on kernel-based density estimation of the posterior, with the mean shift algorithm serving as an efficient gradient estimation and mode-seeking procedure.  ... 
doi:10.1109/lsp.2004.842254 fatcat:7d7sak6jfbg5hnym42mazahvoq

Clusters and water flows: a novel approach to modal clustering through Morse theory [article]

José E. Chacón
2013 arXiv   pre-print
it is quite difficult to specify a population goal to which the data-based clustering algorithms should try to get close.  ...  The problem of finding groups in data (cluster analysis) has been extensively studied by researchers from the fields of Statistics and Computer Science, among others.  ...  The paper by Ray and Lindsay (2005) , in which interesting connections between Morse theory and the topography of multivariate normal mixtures are illustrated, was thought-provoking enough to inspire  ... 
arXiv:1212.1384v2 fatcat:jkkc7xqlo5hynourrvp3knlfrq

Design and Development of Hyper Spectral Image Classification Using Enhanced Mean Shift Segmentation in Image Mining

Dr. S. Thavamani
2017 INTERNATIONAL JOURNAL OF EMERGING TRENDS IN SCIENCE AND TECHNOLOGY  
In our proposed method Enhanced mean shift algorithm is used for segmenting the part of the particular image. Experimental result use accuracy and execution time parameters to show the performance.  ...  Special spectral may have some of the problems such as quality and clarity of the particular image.  ...  Unlike earlier techniques, the enhanced mean shift is a non-parametric technique and hence we need to estimate the gradient of the pdf, f(x), in an iterative manner using kernel density estimation to find  ... 
doi:10.18535/ijetst/v4i8.46 fatcat:7we737gs4zaitkls42xd24bgoy

Study of Clustering Algorithms in Object Tracking and Image Segmentation

Yongfeng Xing, Luo Zhong, Xian Zhong, Mohammad Farukh Hashmi
2022 Wireless Communications and Mobile Computing  
Mean Shift is a kind of clustering algorithm, which is mostly used for target tracking, image segmentation, etc.  ...  This algorithm gets the tracking effect and computation analysis of improved Mean Shift from the perspective of applications, removes or relieves the impact motion has on imaging, improves the quality  ...  Acknowledgments This work was supported in part by the National Science Foundation of China (Grant No. 61303029).  ... 
doi:10.1155/2022/7205929 fatcat:6sj72thusfcgfjatjuhvz3bbou

MSB: A mean-shift-based approach for the analysis of structural variation in the genome

L.-y. Wang, A. Abyzov, J. O. Korbel, M. Snyder, M. Gerstein
2008 Genome Research  
Our method involves mean-shift-based (MSB) procedures; it considers the observed array-CGH signal as sampling from a probabilitydensity function, uses a kernel-based approach to estimate local gradients  ...  It does not require the number of segments as input, nor does its convergence depend on this.  ...  Acknowledgments We thank the anonymous reviewers for their advice and comments. This work was supported by the NIH. We thank Dr. P. Park and his colleagues and the 1000 Genomes Project for data.  ... 
doi:10.1101/gr.080069.108 pmid:19037015 pmcid:PMC2612956 fatcat:kpwqes237zh67kbrtv6wvxn3tq

Segmentation Methods for Synthetic Aperture Radar [chapter]

Shuang Wang
2011 Recent Hurricane Research - Climate, Dynamics, and Societal Impacts  
Since the mean shift vector is aligned with the local gradient estimate, it can define a path leading to a stationary point of the estimated density. Denote by 1,2. ..  ...  In this feature space, the mean shift vector is aligned with the local gradient estimate, it can define a path leading to the local maxima of the density, i.e. the detected modes.  ...  feature subset using a Kuwahara filter; at last, applying mean shift algorithm to gain final results.  ... 
doi:10.5772/16066 fatcat:eywaq55mabhutes7furrkfxkkm
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