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Accelerated convergence using dynamic mean shift
Zhang, K; Kwok, JT; Tang, M; Leonardis, A; Bischof, H; Pinz, A
AbstractMean shift is an iterative mode-seeking algorithm widely used in pattern recognition and computer vision. However, its convergence is sometimes too slow to be practical. In this paper, we improve the convergence speed of mean shift by dynamically updating the sample set during the iterations, and the resultant procedure is called dynamic mean shift (DMS). When the data is locally Gaussian, it can be shown that both the standard and dynamic mean shift algorithms converge to the same optimal solution. However, while standard mean shift only has linear convergence, the dynamic mean shift algorithm has superlinear convergence. Experiments on color image segmentation show that dynamic mean shift produces comparable results as the standard mean shift algorithm, but can significantly reduce the number of iterations for convergence and takes much less time.
WOS HeadingsScience & Technology ; Technology
Indexed ByISTP ; SCI
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Theory & Methods
WOS IDWOS:000237555200020
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Cited Times:6[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Affiliation1.Hong Kong Univ Sci & Technol, Dept Comp Sci, Kowloon, Hong Kong, Peoples R China
2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100080, Peoples R China
Recommended Citation
GB/T 7714
Zhang, K,Kwok, JT,Tang, M,et al. Accelerated convergence using dynamic mean shift[J]. COMPUTER VISION - ECCV 2006, PT 2, PROCEEDINGS,2006,3952:257-268.
APA Zhang, K,Kwok, JT,Tang, M,Leonardis, A,Bischof, H,&Pinz, A.(2006).Accelerated convergence using dynamic mean shift.COMPUTER VISION - ECCV 2006, PT 2, PROCEEDINGS,3952,257-268.
MLA Zhang, K,et al."Accelerated convergence using dynamic mean shift".COMPUTER VISION - ECCV 2006, PT 2, PROCEEDINGS 3952(2006):257-268.
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