On the convergence and consistency of the blurring mean-shift process

被引：0

作者：

Ting-Li Chen

机构：

[1] Academia Sinica,Institute of Statistical Science

来源：

Annals of the Institute of Statistical Mathematics | 2015年 / 67卷

关键词：

Mean-shift; Convergence; Consistency; Clustering; Super robustness ; -Divergence;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

The mean-shift algorithm is a popular algorithm in computer vision and image processing. It can also be cast as a minimum gamma-divergence estimation. In this paper we focus on the “blurring” mean-shift algorithm, which is one version of the mean-shift process that successively blurs the dataset. The analysis of the blurring mean-shift is relatively more complicated compared to the nonblurring version, yet the algorithm convergence and the estimation consistency have not been well studied in the literature. In this paper we prove both the convergence and the consistency of the blurring mean-shift. We also perform simulation studies to compare the efficiency of the blurring and the nonblurring versions of the mean-shift algorithms. Our results show that the blurring mean-shift has more efficiency.

引用

页码：157 / 176

页数：19

共 32 条

[1]

Carreira-Perpinan M.A.(2007)Gaussian mean-shift is an em algorithm IEEE Transactions on Pattern Analysis and Machine Intelligence 29 767-776

[2]

Chang-Chien S.J.(2012)On mean shift-based clustering for circular data Soft Computing 16 1043-1060

[3]

Hung W.L.(1995)Mean shift, mode seeking, and clustering IEEE Transactions on Pattern Analysis and Machine Intelligence 17 790-799

[4]

Yang M.S.(2002)Mean shift: a robust approach toward feature space analysis IEEE Transactions on Pattern Analysis and Machine Intelligence 24 603-619

[5]

Cheng Y.Z.(2005)Mean shift is a bound optimization IEEE Transactions on Pattern Analysis and Machine Intelligence 27 471-474

[6]

Comaniciu D.(2013)Mean shift-based clustering of remotely sensed data with agricultural and land-cover applications International Journal of Remote Sensing 34 6037-6053

[7]

Meer P.(2008)Robust parameter estimation with a small bias against heavy contamination Journal of Multivariate Analysis 99 2053-2081

[8]

Fashing M.(1975)The estimation of the gradient of a density function, with applications in pattern recognition IEEE Transactions on Information Theory 21 32-40

[9]

Tomasi C.(2012)Mean shift trackers with cross-bin metrics IEEE Transactions on Pattern Analysis and Machine Intelligence 34 695-706

[10]

Friedman L.(2007)A note on the convergence of the mean shift Pattern Recognition 40 1756-1762

← 1 2 3 4 →