Image and Video Segmentation by Anisotropic Kernel Mean Shift

  • Jue Wang
  • Bo Thiesson
  • Yingqing Xu
  • Michael Cohen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3022)


Mean shift is a nonparametric estimator of density which has been applied to image and video segmentation. Traditional mean shift based segmentation uses a radially symmetric kernel to estimate local density, which is not optimal in view of the often structured nature of image and more particularly video data. In this paper we present an anisotropic kernel mean shift in which the shape, scale, and orientation of the kernels adapt to the local structure of the image or video. We decompose the anisotropic kernel to provide handles for modifying the segmentation based on simple heuristics. Experimental results show that the anisotropic kernel mean shift outperforms the original mean shift on image and video segmentation in the following aspects: 1) it gets better results on general images and video in a smoothness sense; 2) the segmented results are more consistent with human visual saliency; 3) the algorithm is robust to initial parameters.


Image Segmentation Video Data Shift Point Video Segmentation Color Domain 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Jue Wang
    • 1
  • Bo Thiesson
    • 1
  • Yingqing Xu
    • 1
  • Michael Cohen
    • 1
  1. 1.Microsoft Research (Asia and Redmond) 

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