Visual language model for face clustering in consumer photos

Wei Ta Chu, Ya Lin Lee, Jen Yu Yu

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

8 Citations (Scopus)

Abstract

For consumer photos, this work clusters faces with large variations in lighting, pose, and expression. After matching face images by local feature points, we transform matching situations into a novel representation called visual sentences. Then, visual language models are constructed to describe the dependency of image patches on faces. With the probabilistic framework, we develop a clustering algorithm to group the same individual's face images into the same cluster. An interesting observation about evaluating face clustering performance is proposed, and we demonstrate the superiority of the proposed visual language model approach.

Original languageEnglish
Title of host publicationMM'09 - Proceedings of the 2009 ACM Multimedia Conference, with Co-located Workshops and Symposiums
Pages625-628
Number of pages4
DOIs
Publication statusPublished - 2009 Dec 28
Event17th ACM International Conference on Multimedia, MM'09, with Co-located Workshops and Symposiums - Beijing, China
Duration: 2009 Oct 192009 Oct 24

Publication series

NameMM'09 - Proceedings of the 2009 ACM Multimedia Conference, with Co-located Workshops and Symposiums

Conference

Conference17th ACM International Conference on Multimedia, MM'09, with Co-located Workshops and Symposiums
CountryChina
CityBeijing
Period09-10-1909-10-24

All Science Journal Classification (ASJC) codes

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Software

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  • Cite this

    Chu, W. T., Lee, Y. L., & Yu, J. Y. (2009). Visual language model for face clustering in consumer photos. In MM'09 - Proceedings of the 2009 ACM Multimedia Conference, with Co-located Workshops and Symposiums (pp. 625-628). (MM'09 - Proceedings of the 2009 ACM Multimedia Conference, with Co-located Workshops and Symposiums). https://doi.org/10.1145/1631272.1631372