Local Best-Fit (LBF) Algorithm

Local Best-Fit (LBF) Algorithm


Brief Introduction
LBF is a very simple geometric method for hybrid
linear modeling. It is based on selecting a set of local best fit
flats, where the size of the local neighborhoods is determined
automatically by the Jones’ beta_2 numbers. It can then select the
global K flats by many of the existing strategies. In the current code
we used an
L_1 minimization procedure, but other
successful procedures will be provided online soon.
If the
number of clusters K is unknown, we then apply the SOD algorithm
provided below.


Paper
Hybrid Linear Modeling via Local Best-fit Flats
,
International Journal of Computer Vision Volume 100, Issue 3
(2012), Page 217-240


Randomized hybrid linear modeling by local best-fit flats, 2010 IEEE Conference on Computer Vision and Pattern Recognition
(CVPR) (13-18 June 2010), pp. 1927-1934


Matlab Code

Codes for other algorithms in the paper

Supplemental Data

 

  1. Artificial data: generated by using ‘generate_samples.m’ which is
    contained in the GPCA-voting
    folder
  2. Real data: Motion Segmentation (http://www.vision.jhu.edu/data/hopkins155/)

Contact

Acknowledgement

  • The research described here was supported
    by
    NSF grants DMS-0612608,
    DMS-0811203 and DMS-0915064.