Semi-Supervised Learning with Constraints for Person Identification in Multimedia Data

Martin Bauml, Makarand Tapaswi, Rainer Stiefelhagen

CVPR 2013

doi:10.1109/CVPR.2013.462 /cvpr/2013/bauml2013cvpr-semisupervised/

Abstract

We address the problem of person identification in TV series. We propose a unified learning framework for multiclass classification which incorporates labeled and unlabeled data, and constraints between pairs of features in the training. We apply the framework to train multinomial logistic regression classifiers for multi-class face recognition. The method is completely automatic, as the labeled data is obtained by tagging speaking faces using subtitles and fan transcripts of the videos. We demonstrate our approach on six episodes each of two diverse TV series and achieve state-of-the-art performance.

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Cite

Text

Bauml et al. "Semi-Supervised Learning with Constraints for Person Identification in Multimedia Data." Conference on Computer Vision and Pattern Recognition, 2013. doi:10.1109/CVPR.2013.462

Markdown

[Bauml et al. "Semi-Supervised Learning with Constraints for Person Identification in Multimedia Data." Conference on Computer Vision and Pattern Recognition, 2013.](https://mlanthology.org/cvpr/2013/bauml2013cvpr-semisupervised/) doi:10.1109/CVPR.2013.462

BibTeX

@inproceedings{bauml2013cvpr-semisupervised,
  title     = {{Semi-Supervised Learning with Constraints for Person Identification in Multimedia Data}},
  author    = {Bauml, Martin and Tapaswi, Makarand and Stiefelhagen, Rainer},
  booktitle = {Conference on Computer Vision and Pattern Recognition},
  year      = {2013},
  doi       = {10.1109/CVPR.2013.462},
  url       = {https://mlanthology.org/cvpr/2013/bauml2013cvpr-semisupervised/}
}