Ranking with uncertain labels

Yan Shuicheng, Wang Huan, Thomas S. Huang, Yang Qiong, Tang Xiaoou

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

Abstract

Most techniques for image analysis consider the image labels fixed and without uncertainty. In this paper, we address the problem of ordinal/rank label prediction based on training samples with uncertain labels. First, the core ranking model is designed as the bilinear fusing of multiple candidate kernels. Then, the parameters for feature selection and kernel selection are learned by maximum a posteriori for given samples and uncertain labels. The convergency provable Expectation-Maximization (EM) method is used for inferring these parameters. The effectiveness of the proposed algorithm is finally validated by the extensive experiments on age ranking task. The FG-NET and Yamaha aging database are used for the experiments, and our algorithm significantly outperforms those state-of-the-art algorithms ever reported in literature.

Original languageEnglish (US)
Title of host publicationProceedings of the 2007 IEEE International Conference on Multimedia and Expo, ICME 2007
Pages96-99
Number of pages4
StatePublished - 2007
EventIEEE International Conference onMultimedia and Expo, ICME 2007 - Beijing, China
Duration: Jul 2 2007Jul 5 2007

Publication series

NameProceedings of the 2007 IEEE International Conference on Multimedia and Expo, ICME 2007

Other

OtherIEEE International Conference onMultimedia and Expo, ICME 2007
Country/TerritoryChina
CityBeijing
Period7/2/077/5/07

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Software

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