Bipolar grouping

Jiang Xu*, Junsong Yuan, Ying Wu

*Corresponding author for this work

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

1 Scopus citations

Abstract

Most affinity-based grouping methods only model the inclusive relation among the data. When the data set contains a significant amount of noise data that should not be included in any clusters, these methods are likely to lead to undesired results. To address this issue, this paper presents a new approach called bipolar grouping that is targeted on extracting the groups from the data while excluding the noise. This new approach incorporates both inclusive and exclusive relations among data, and a fixed-point procedure is proposed to find the stable groups. Its effectiveness and general applicability are demonstrated in two applications, including discovering common objects in images and tracking targets in clutter.

Original languageEnglish (US)
Title of host publication2010 IEEE International Conference on Multimedia and Expo, ICME 2010
Pages54-59
Number of pages6
DOIs
StatePublished - Nov 22 2010
Event2010 IEEE International Conference on Multimedia and Expo, ICME 2010 - Singapore, Singapore
Duration: Jul 19 2010Jul 23 2010

Publication series

Name2010 IEEE International Conference on Multimedia and Expo, ICME 2010

Other

Other2010 IEEE International Conference on Multimedia and Expo, ICME 2010
CountrySingapore
CitySingapore
Period7/19/107/23/10

Keywords

  • Bipolar grouping
  • Common pattern discovery
  • Visual object tracking

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Software

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  • Cite this

    Xu, J., Yuan, J., & Wu, Y. (2010). Bipolar grouping. In 2010 IEEE International Conference on Multimedia and Expo, ICME 2010 (pp. 54-59). [5583062] (2010 IEEE International Conference on Multimedia and Expo, ICME 2010). https://doi.org/10.1109/ICME.2010.5583062