Efficient Mean-shift Clustering Using Gaussian KD-Tree
Mean shift is a popular approach for data clustering, however, the high computational complexity of the mean shift procedure limits its practical applications in high dimensional and large data set clustering. In this paper, we propose an efficient method that allows mean shift clustering performed...
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| Published in: | Computer graphics forum Vol. 29; no. 7; pp. 2065 - 2073 |
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| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
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Oxford, UK
Blackwell Publishing Ltd
01.09.2010
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| ISSN: | 0167-7055, 1467-8659 |
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| Abstract | Mean shift is a popular approach for data clustering, however, the high computational complexity of the mean shift procedure limits its practical applications in high dimensional and large data set clustering. In this paper, we propose an efficient method that allows mean shift clustering performed on large data set containing tens of millions of points at interactive rate. The key in our method is a new scheme for approximating mean shift procedure using a greatly reduced feature space. This reduced feature space is adaptive clustering of the original data set, and is generated by applying adaptive KD‐tree in a high‐dimensional affinity space. The proposed method significantly reduces the computational cost while obtaining almost the same clustering results as the standard mean shift procedure. We present several kinds of data clustering applications to illustrate the efficiency of the proposed method, including image and video segmentation, static geometry model and time‐varying sequences segmentation. |
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| AbstractList | Mean shift is a popular approach for data clustering, however, the high computational complexity of the mean shift procedure limits its practical applications in high dimensional and large data set clustering. In this paper, we propose an efficient method that allows mean shift clustering performed on large data set containing tens of millions of points at interactive rate. The key in our method is a new scheme for approximating mean shift procedure using a greatly reduced feature space. This reduced feature space is adaptive clustering of the original data set, and is generated by applying adaptive KD‐tree in a high‐dimensional affinity space. The proposed method significantly reduces the computational cost while obtaining almost the same clustering results as the standard mean shift procedure. We present several kinds of data clustering applications to illustrate the efficiency of the proposed method, including image and video segmentation, static geometry model and time‐varying sequences segmentation. AbstractMean shift is a popular approach for data clustering, however, the high computational complexity of the mean shift procedure limits its practical applications in high dimensional and large data set clustering. In this paper, we propose an efficient method that allows mean shift clustering performed on large data set containing tens of millions of points at interactive rate. The key in our method is a new scheme for approximating mean shift procedure using a greatly reduced feature space. This reduced feature space is adaptive clustering of the original data set, and is generated by applying adaptive KD-tree in a high-dimensional affinity space. The proposed method significantly reduces the computational cost while obtaining almost the same clustering results as the standard mean shift procedure. We present several kinds of data clustering applications to illustrate the efficiency of the proposed method, including image and video segmentation, static geometry model and time-varying sequences segmentation. |
| Author | Xiao, Chunxia Liu, Meng |
| Author_xml | – sequence: 1 givenname: Chunxia surname: Xiao fullname: Xiao, Chunxia organization: The School of Computer, Wuhan University, Wuhan, 430072, China – sequence: 2 givenname: Meng surname: Liu fullname: Liu, Meng organization: The School of Computer, Wuhan University, Wuhan, 430072, China |
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ACM SIGGRAPH Computer Graphics 25, 4 (1991), 206. – reference: Carreira-Perpinán M.: Acceleration strategies for Gaussian mean-shift image segmentation. In CVPR (2006), vol. 1. – reference: Elgammal A., Duraiswami R., Davis L.: Efficient kernel density estimation using the fast gauss transform with applications to color modeling and tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence 25, 11 (2003), 1499-1504. – reference: Barash D., Comaniciu D.: A common framework for nonlinear diffusion, adaptive smoothing, bilateral filtering and mean shift. Image and Vision Computing 22, 1 (2004), 73-81. – reference: Cheng Y.: Mean shift, mode seeking, and clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence 17, 8 (1995), 790-799. – reference: DeMenthon D.: Spatio-temporal segmentation of video by hierarchical mean shift analysis. Language 2 (2002). – reference: Buades A., Coll B., Morel J.: A non-local algorithm for image denoising. In CVPR 2005 (2005), pp. 60-65. – reference: Fleishman S., Drori I., Cohen-Or D.: Bilateral mesh denoising. ACM Transactions on Graphics (TOG) 22, 3 (2003), 950-953. – reference: Jones T., Durand F., Desbrun M.: Non-iterative, feature-preserving mesh smoothing. ACM Transactions on Graphics 22, 3 (2003), 943-949. – reference: Adams A., Gelfand N., Dolson L., Levoy M.: Gaussian kd-trees for fast high-dimensional filtering. ACM Transactions on Graphics (TOG) 28, 3 (2009), 21. – reference: Arya S., Mount D., Netanyahu N., Silverman R., WU A.: An optimal algorithm for approximate near-est neighbor searching fixed dimensions. Journal of the ACM (JACM) 45, 6 (1998), 891-923. – reference: Comaniciu D., Ramesh V., Meer P.: Kernel-based object tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence 25, 5 (2003), 564-577. – volume: 1 year: 2006 article-title: Acceleration strategies for Gaussian mean‐shift image segmentation publication-title: CVPR – volume: 22 start-page: 943 issue: 3 year: 2003 end-page: 949 article-title: Non‐iterative, feature‐preserving mesh smoothing publication-title: ACM Transactions on Graphics – start-page: 769 year: 2002 end-page: 776 – volume: 243 year: 2005 – start-page: 1 year: 2007 end-page: 8 – volume: 3 start-page: 447 year: 2003 end-page: 450 – volume: 17 start-page: 790 issue: 8 year: 1995 end-page: 799 article-title: Mean shift, mode seeking, and clustering publication-title: IEEE Transactions on Pattern Analysis and Machine Intelligence – start-page: 574 year: 2004 end-page: 583 – year: 2007 – start-page: 664 year: 2003 end-page: 671 – volume: 27 start-page: 40 issue: 3 year: 2008 article-title: Appprop: all‐pairs appearances‐pace edit propagation publication-title: ACM Trans. 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| Snippet | Mean shift is a popular approach for data clustering, however, the high computational complexity of the mean shift procedure limits its practical applications... AbstractMean shift is a popular approach for data clustering, however, the high computational complexity of the mean shift procedure limits its practical... |
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| SubjectTerms | Approximation Clustering Computational efficiency Computer graphics Datasets Gaussian I.4 [Computing methodologies]: Image Processing and Computer Vision-Applications Image coding Image processing systems Interactive Mathematical models Normal distribution Segmentation Studies |
| Title | Efficient Mean-shift Clustering Using Gaussian KD-Tree |
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