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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Vydané v:Computer graphics forum Ročník 29; číslo 7; s. 2065 - 2073
Hlavní autori: Xiao, Chunxia, Liu, Meng
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Oxford, UK Blackwell Publishing Ltd 01.09.2010
Predmet:
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
ISSN:0167-7055, 1467-8659
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Shrnutí: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.
Bibliografia:ark:/67375/WNG-ZC5H37S3-W
istex:F386FF631523E7F3CE723560CBFD898EC5B9D87E
ArticleID:CGF1793
SourceType-Scholarly Journals-1
ObjectType-Feature-1
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ISSN:0167-7055
1467-8659
DOI:10.1111/j.1467-8659.2010.01793.x