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基于自适应带宽的快速动态高斯核均值漂移算法 被引量：2

Fast dynamic Gaussian mean-shift algorithm based on adaptive bandwidth
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摘要 由核密度估计推导获得的高斯核均值漂移算法因收敛速度慢在应用中效率不高.本文提出基于自适应带宽的动态更新改进方法.首先采用空间离散方法对数据集化简,然后引入动态更新机制,每次迭代后将数据集更新到均值点,并将聚集在一起的数据点用一个收敛点表示,同时根据数据集直径的变化,自适应地计算各向异性的带宽参数.实验表明,该方法提高了算法的收敛速度,降低了计算复杂度. The Gaussian kernel mean-shift algorithm which is deduced from kernel density estimation has not been widely employed in applications because of its low convergence rate. We propose a dynamic mean-shift algorithm based on adaptive bandwidth. The number of data sets is reduced by adaptive space discretization; the convergence rate is improved by dynamically updating the data set, and the efficiency is promoted by replacing the overlapping points with a special point in the iterations. The anisotropic bandwidth is updated according to the diameter of the data set. Experiments validate the improvement of the convergence rate of Gaussian mean-shift with lower complexity in computation.
作者 周芳芳 樊晓平 叶榛
机构地区 中南大学信息科学与工程学院 清华大学智能技术与系统国家重点实验室
出处 《控制理论与应用》 EI CAS CSCD 北大核心 2008年第4期608-612,共5页 Control Theory & Applications
基金 国家自然科学基金(69975003)．
关键词 均值漂移 高斯核 核密度估计 自适应带宽 mean shift Gaussian kernel kernel density estimation adaptive bandwidth
分类号 TP301.6 [自动化与计算机技术—计算机系统结构]
  • 相关文献

参考文献8

  • 1CHENG Y Z. Mean shift, mode seeking, and clustering[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1995, 17(8): 790 - 799.
  • 2COMANICIU D, MEER P. Mean shift: A robust approach toward feature space analysis[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(5): 603 - 619.
  • 3李乡儒,吴福朝,胡占义.均值漂移算法的收敛性[J].软件学报,2005,16(3):365-374. 被引量：88
  • 4FASHING M, TOMASI C. Mean shift is a bound optimization[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(3): 471 - 474.
  • 5SHEN C, BROOKS M J. Adaptive over-relaxed mean shift[C] //Proceedings of the 8th International Symposium on Signal Processing and Its Applications. New York: IEEE CS Press, 2005:28 - 31.
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  • 7CARREIRA-PERPINAN M A. Acceleration strategies for Gaussian mean-shift image segmentation[C]//Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. New York: IEEE CS Press, 2006:543 - 549.
  • 8COMANICIU D. An algorithm for data-driven bandwidth selection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(2): 281 -288.

二级参考文献22

  • 1Comaniciu 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.
  • 2Comaniciu 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.
  • 3Comaniciu 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.
  • 4Comaniciu 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.
  • 5Bradski 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.
  • 6Comaniciu 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.
  • 7Comaniciu D. An algorithm for data-driven bandwidth selection. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2003, 25(2):281-288.
  • 8Comaniciu 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.
  • 9Comaniciu D, Ramesh V, Meer P. Kernel-Based object tracking. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2003, 25(5):564-575.
  • 10Collins 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.

共引文献87

同被引文献21

  • 1Fukunaga K, Hostetler L D. The estimation of the gradient of a density function, with applications in pattern recognition[J]. IEEE Trans on Information Theory, 1975, 21(1): 32-40.
  • 2Cheng Y Z. Mean shift, mode seeking, and clustering[J]. IEEE Trans on Pattern Analysis and Machine Intelligence, 1995, 17(8): 790-799.
  • 3Yang C J, Duraiswami R, De Menthon D, et al. Mean-shift analysis using Quasi-Newton methods[C]. Proc of IEEE Int Conf on Image Processing. Barcelona, 2003, 3: 447-450.
  • 4Carreira-PerpinOn M A. Acceleration strategies for Gaussian mean shift image segmentation[C]. Proc of IEEE Conf on Computer Vision and Pattern Recognition. New York, 2006, 1: 1160-1167.
  • 5Shen C, Brooks M J, Hengel A. Fast global kernel density mode seeking with application to localisation and tracking[C]. Proc of the 10th IEEE Int Conf on Computer Vision. Beijing, 2005, 2: 1516-1523.
  • 6Shen C, Brooks M J. Adaptive over-relaxed mean shift[C]. Proc of the 8th Int Symposium on Signal Processing and Its Applications. Sydney, 2005, 2: 575-578.
  • 7Georgescu B, Shimshoni I, Meer P. Mean shift based clustering in high dimensions: A texture classification example[C]. Proc of ICCV. Berlin, 2003, 1: 456-463.
  • 8Zhang K, Tang M, Kwok J T. Applying neighborhood consistency for fast clustering and kernel density estimation[C]. Proc of 2005 IEEE Comp Society Conf on Computer Vision and Pattern Recognition. Washington DC, 2005, 2: 1001-1007.
  • 9Elgammal A, Duraiswami R, Davis L. Efficient nonparametric adaptive color modeling using fast Gauss transform[C]. Proc of Int Conf on Computer Vision and Pattern Recognition. Hawaii, 2001, 2: 563-570.
  • 10Yang C, Duraiswami R, Gumerov N, et al. Improved fast Gauss transform and efficient kernel density estimation[C]. Proc of the 9th IEEE Int Conf on Computer Vision. Berlin, 2003, 1: 664-671.

引证文献2

二级引证文献5

控制理论与应用
2008年 第4期

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