Accelerated convergence using dynamic mean shift
文献类型：期刊论文
| 作者 | Zhang, K; Kwok, JT; Tang, M ; Leonardis, A; Bischof, H; Pinz, A
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| 刊名 | COMPUTER VISION - ECCV 2006, PT 2, PROCEEDINGS
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| 出版日期 | 2006 |
| 卷号 | 3952页码:257-268 |
| 英文摘要 | Mean 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标题词 | Science & Technology ; Technology |
| 类目[WOS] | Computer Science, Artificial Intelligence ; Computer Science, Theory & Methods |
| 研究领域[WOS] | Computer Science |
| 收录类别 | ISTP ; SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000237555200020 |
| 公开日期 | 2015-12-24 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/9341]  |
| 专题 | 自动化研究所_09年以前成果 |
| 作者单位 | 1.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 |
| 推荐引用方式 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. |
入库方式： OAI收割
来源：自动化研究所
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