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

We address the problem of person identification in TV series. We propose a unified learning framework for multi-class classification which incorporates labeled and unlabeled data, and constraints between pairs of features in the training. We apply the framework to train multinomial logistic regressi...

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Bibliographic Details
Published in2013 IEEE Conference on Computer Vision and Pattern Recognition pp. 3602 - 3609
Main Authors Bauml, Martin, Tapaswi, Makarand, Stiefelhagen, Rainer
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2013
Subjects
Entropy
Face
Joints
Training
Training data
Videos
Online AccessGet full text
ISSN1063-6919
1063-6919
DOI10.1109/CVPR.2013.462

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Summary:We address the problem of person identification in TV series. We propose a unified learning framework for multi-class 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.
ISSN:1063-6919
1063-6919
DOI:10.1109/CVPR.2013.462