Learning based on kernel discriminant-EM algorithm for image classification

In image classification and other learning-based object recognition tasks, it is often tedious and expensive to label large training data sets. Discriminant-EM (DEM), proposed as a semi-supervised learning framework, takes both labeled and unlabeled data to learn classifiers. The paper extends the linear DEM to a nonlinear kernel algorithm, KDEM, and evaluates KDEM on both benchmark image databases and synthetic data. Various comparisons with other state-of-the-art learning techniques are investigated.

[1]  Nicu Sebe,et al.  Learning Bayesian network classifiers for facial expression recognition both labeled and unlabeled data , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[2]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[3]  Sebastian Thrun,et al.  Text Classification from Labeled and Unlabeled Documents using EM , 2000, Machine Learning.

[4]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory, Second Edition , 2000, Statistics for Engineering and Information Science.

[5]  B. Scholkopf,et al.  Fisher discriminant analysis with kernels , 1999, Neural Networks for Signal Processing IX: Proceedings of the 1999 IEEE Signal Processing Society Workshop (Cat. No.98TH8468).

[6]  Qi Tian,et al.  Discriminant-EM algorithm with application to image retrieval , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[7]  David G. Stork,et al.  Pattern classification, 2nd Edition , 2000 .

[8]  Nicu Sebe,et al.  A new analysis of the value of unlabeled data in semi-supervised learning for image retrieval , 2004, 2004 IEEE International Conference on Multimedia and Expo (ICME) (IEEE Cat. No.04TH8763).

[9]  Lei Wang,et al.  Bootstrapping SVM active learning by incorporating unlabelled images for image retrieval , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[10]  Thomas S. Huang,et al.  Small sample learning during multimedia retrieval using BiasMap , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[11]  Alexander J. Smola,et al.  Learning with kernels , 1998 .

[12]  Marcel Worring,et al.  Content-Based Image Retrieval at the End of the Early Years , 2000, IEEE Trans. Pattern Anal. Mach. Intell..