Context-aware clustering

Junsong Yuan*, Ying Wu

*Corresponding author for this work

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

21 Scopus citations

Abstract

Most existing methods of semi-supervised clustering introduce supervision from outside, e.g., manually label some data samples or introduce constrains into clustering results. This paper studies an interesting problem: can the supervision come from inside, i.e., the unsupervised training data themselves? If the data samples are not independent, we can capture the contextual information reflecting the dependency among the data samples, and use it as supervision to improve the clustering. This is called context-aware clustering. The investigation is substantialized on two scenarios of (1) clustering primitive visual features (e.g., SIFT features) with help of spatial contexts, and (2) clustering '0'-'9' hand written digits with help of contextual patterns among different types of features. Our context-aware clustering can be well formulated in a closed-form, where the contextual information serves as a regularization term to balance the data fidelity in original feature space and the influences of contextual patterns. A nested-EM algorithm is proposed to obtain an efficient solution, which proves to converge. By exploring the dependent structure of the data samples, this method is completely unsupervised, as no outside supervision is introduced.

Original languageEnglish (US)
Title of host publication26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR
DOIs
StatePublished - Sep 23 2008
Event26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR - Anchorage, AK, United States
Duration: Jun 23 2008Jun 28 2008

Publication series

Name26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR

Other

Other26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR
CountryUnited States
CityAnchorage, AK
Period6/23/086/28/08

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Control and Systems Engineering

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  • Cite this

    Yuan, J., & Wu, Y. (2008). Context-aware clustering. In 26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR [4587348] (26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR). https://doi.org/10.1109/CVPR.2008.4587348