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    Abstract:

    Mean shift is an effective iterative algorithm widely used in clustering, tracking, segmentation, discontinuity preserving smoothing, filtering, edge detection, and information fusion etc. However, its convergence, a key property of any iterative method, has not been rigorously proved till now. In this paper, the traditional mean shift algorithm is first extended to account for both the local property at different sampling points and the anisotropic property at different directions, then a rigorous convergence proof is provided under these extended conditions. Finally, some approaches to adaptively selecting the algorithm’s parameters are outlined. The results in this paper contribute substantially to the establishment of a sound theoretical foundation for the mean shift algorithm.

    Reference
    [1]NummiaroK, Koller-Meier E, Van Gool L. An adaptive color-based particle filter. Image and Vision Computing, 2002,21(1): 99-110.
    [2]Comaniciu D, Ramesh V, Meer P. Real-Time tracking of non-rigid objects using mean shift. In: Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition (CVPR). 2000. 142-149.
    [3]Comaniciu D, Ramesh V. Mean shift and optimal prediction for efficient object tracking. In: Mojsilovic A, Hu J, eds. Proc. of the IEEE Int'l Conf. on Image Processing (ICIP). 2000. 70-73.
    [4]Comaniciu D, Ramesh V, Meer P. The variable bandwidth mean shift and data-driven scale selection. In: Proc. of the IEEE Int'l Conf. on Computer Vision (ICCV). 2001. 438-445. http://citeseer.csail.mit.edu/comaniciu01variable.html
    [5]Comaniciu D, Meer P. Mean shift analysis and applications. In: Proc. of the IEEE Int'l Conf. on Computer Vision (ICCV). 1999. 1197-1203. http://citeseer.ist.psu.edu/comaniciu00realtime.html
    [6]Bradski GR. Computer vision face tracking for use in a perceptual user interface. Intel Technology Journal, 1998. http://developer. intel.com/technology/itj/q21998/articles/art_2.htm
    [7]Comaniciu D, Meer P. Mean shift: A robust approach toward feature space analysis. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2002,24(5):603-619.
    [8]Comaniciu D. An algorithm for data-driven bandwidth selection. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2003, 25(2):281-288.
    [9]Comaniciu D. Nonparametric information fusion for motion estimation. In: Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition (CVPR). 2003. 59-66. http://csdl.computer.org/comp/proceedings/cvpr/2003/1900/01/190010059abs.htm
    [10]Comaniciu D, Ramesh V, Meer P. Kernel-Based object tracking. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2003, 25(5):564-575.
    [11]Collins RT. Mean-Shift blob tracking through scale space. In: Proc. of the Conf. on Computer Vision and Pattern Recognition (CVPR). 2003. 18-20. http://csdl.computer.org/comp/proceedings/cvpr/2003/1900/02/190020234abs.htm
    [12]Bradski GR. Real time face and object tracking as a component of a perceptual user interface. In: Proc. of the 4th IEEE Workshop on Applications of Computer Vision (WACV). 1998. 19-21. http://csdl.computer.org/comp/proceedings/wacv/1998/8606/00/8606 0214abs.htm
    [13]Comaniciu D, Meer P. Robust analysis of feature spaces: Color image segmentation. In: Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition (CVPR). 1997. 750-755. http://csdl.computer.org/comp/proceedings/cvpr/1997/7822/00/782207 50abs.htm
    [14]Zhou XS, Comaniciu D, Krishnan S. An information fusion framework for robust shape tracking. In: Proc. of the 3rd Int'l Workshop on Statistical and Computational Theories of Vision. 2003. http://csdl.computer.org/comp/trans/tp/2005/01/i0115abs.htm
    [15]Barash D, Comaniciu D. A common framework for nonlinear diffusion, adaptive smoothing, bilateral filtering and mean shift. Image and Video Computing, 2003,22(1):73-81.
    [16]Comaniciu D, Meer P. Distribution free decomposition of multivariate data. Pattern Analysis and Applications, 1999,2(1):22-30.
    [17]Comaniciu D, Ramesh V. Robust detection and tracking of human faces with an active camera. In: Proc. of the IEEE Int'l Workshop on Visual Surveillance. 2000. 11-18. http://csdl.computer.org/dl/proceedings/vs/2000/0698/00/06980011.pdf
    [18]Comaniciu D. Image segmentation using clustering with saddle point detection. In: Proc. of the IEEE Int'l Conf. on Image Processing (ICIP). 2002. 297-300. http://ieeexplore.ieee.org/xpl/abs_free.jsp-arNumber=1038964
    [19]Comaniciu D, Ramesh V, Bue AD. Multivariate saddle point detection for statistical clustering. In: Proc. of the European Conf. Computer Vision (ECCV). 2002. 561-576. http://link.springer.de/link/service/series/0558/bibs/2352/23520561.htm
    [20]Cheng YZ. Mean shift, mode seeking, and clustering. IEEE Trans. on Pattern Analysis and Machine Intelligence, 1995,17(8): 790-799.
    [21]Xue CX. Real Function Theory and Functional Analysis. Beijing: Higher Education Press, 1993. 187-188 (in Chinese).
    [22]Peters CA, Valafar F. Comparison of three nonparametric density estimation techniques using Bayes' classifier applied to microarray data analysis. In: Proc. of the Int'l Conf. on Mathematics and Engineering Techniques in Medicine and Biological Sciences. 2003. 119-125. http://www-rohan.sdsu.edu/~faramarz/papers/ME281-Peters-Valafar.pdf
    [23]Bian ZQ, Zhang XG. Pattern Recognition. 2nd ed., Beijing: Tsinghua University Press, 2000. 65-72 (in Chinese).
    [24]薛昌兴.实变函数与泛函分析.北京:高等教育出版社,1993.187-188.
    [25]边肇祺,张学工.模式识别.北京:清华大学出版社,2000.65-72.
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李乡儒,吴福朝,胡占义.均值漂移算法的收敛性.软件学报,2005,16(3):365-374

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  • Received:May 30,2004
  • Revised:August 10,2004
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