Publication Type

Journal Article

Version

publishedVersion

Publication Date

1-2008

Abstract

Face annotation in images and videos enjoys many potential applications in multimedia information retrieval. Face annotation usually requires many training data labeled by hand in order to build effective classifiers. This is particularly challenging when annotating faces on large-scale collections of media data, in which huge labeling efforts would be very expensive. As a result, traditional supervised face annotation methods often suffer from insufficient training data. To attack this challenge, in this paper, we propose a novel Transductive Kernel Fisher Discriminant (TKFD) scheme for face annotation, which outperforms traditional supervised annotation methods with few training data. The main idea of our approach is to solve the Fisher's discriminant using deformed kernels incorporating the information of both labeled and unlabeled data. To evaluate the effectiveness of our method, we have conducted extensive experiments on three types of multimedia testbeds: the FRGC benchmark face dataset, the Yahoo! web image collection, and the TRECVID video data collection. The experimental results show that our TKFD algorithm is more effective than traditional supervised approaches, especially when there are very few training data

Keywords

Face annotation, image annotation, kernel Fisher discriminant, multimedia information retrieval, supervised learning, transductive kernel Fisher discriminant, transductive learning

Discipline

Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

IEEE Transactions on Multimedia

Volume

10

Issue

1

First Page

86

Last Page

96

ISSN

1520-9210

Identifier

10.1109/TMM.2007.911245

Publisher

IEEE

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/TMM.2007.911245

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