Improved mean shift algorithm with heterogeneous node weights

Ji Won Yoon, Simon P. Wilson

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

The conventional mean shift algorithm has been known to be sensitive to selecting a bandwidth. We present a robust mean shift algorithm with heterogeneous node weights that come from a geometric structure of a given data set. Before running MS procedure, we reconstruct un-normalized weights (a rough surface of data points) from the Delaunay Triangulation. The un-normalized weights help MS to avoid the problem of failing of misled mean shift vectors. As a result, we can obtain a more robust clustering result compared to the conventional mean shift algorithm. We also propose an alternative way to assign weights for large size datasets and noisy datasets.

Original languageEnglish
Title of host publicationProceedings - 2010 20th International Conference on Pattern Recognition, ICPR 2010
Pages4222-4225
Number of pages4
DOIs
Publication statusPublished - 2010
Event2010 20th International Conference on Pattern Recognition, ICPR 2010 - Istanbul, Turkey
Duration: 2010 Aug 232010 Aug 26

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651

Other

Other2010 20th International Conference on Pattern Recognition, ICPR 2010
CountryTurkey
CityIstanbul
Period10/8/2310/8/26

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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  • Cite this

    Yoon, J. W., & Wilson, S. P. (2010). Improved mean shift algorithm with heterogeneous node weights. In Proceedings - 2010 20th International Conference on Pattern Recognition, ICPR 2010 (pp. 4222-4225). [5597740] (Proceedings - International Conference on Pattern Recognition). https://doi.org/10.1109/ICPR.2010.1026