Self-supervised learning for object recognition based on kernel discriminant-EM algorithm

Y. Wu*, T. S. Huang, K. Toyama

*Corresponding author for this work

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

25 Scopus citations

Abstract

It is often tedious and expensive to label large training data sets for learning-based object recognition systems. This problem could be alleviated by self-supervised learning techniques, which take a hybrid of labeled and unlabeled training data to learn classifiers. Discriminant-EM (D-EM) proposed a framework for such tasks and current D-EM algorithm employed linear discriminant analysis. However, the algorithm is limited by its dependence on linear transformations. This paper extends the linear D-EM to nonlinear kernel algorithm, Kernel D-EM, based on kernel multiple discriminant analysis (KMDA). KMDA provides better ability to simplify the probabilistic structures of data distributions in a discrimination space. We propose two novel data-sampling schemes for efficient training of kernel discriminants. Experimental results show that classifiers using KMDA learning compare with SVM performance on standard benchmark tests, and that Kernel D-EM outperforms a variety of supervised and semi-supervised learning algorithms for a hand-gesture recognition task and fingertip tracking task.

Original languageEnglish (US)
Title of host publicationProceedings of the IEEE International Conference on Computer Vision
Pages275-280
Number of pages6
Volume1
StatePublished - Jan 1 2001
Event8th International Conference on Computer Vision - Vancouver, BC, United States
Duration: Jul 9 2001Jul 12 2001

Other

Other8th International Conference on Computer Vision
CountryUnited States
CityVancouver, BC
Period7/9/017/12/01

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

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  • Cite this

    Wu, Y., Huang, T. S., & Toyama, K. (2001). Self-supervised learning for object recognition based on kernel discriminant-EM algorithm. In Proceedings of the IEEE International Conference on Computer Vision (Vol. 1, pp. 275-280)