Learning based on kernel discriminant-EM algorithm for image classification

Author

Tian, Qi ; Yu, Jie ; Wu, Ying ; Huang, Thomas S.

Author_Institution

Dept. of Comput. Sci., Texas Univ., San Antonio, TX, USA

Volume

5

fYear

2004

fDate

17-21 May 2004

Abstract

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.

Keywords

content-based retrieval; image classification; image retrieval; learning (artificial intelligence); optimisation; content-based image retrieval; image classification; kernel discriminant-EM algorithm; learning-based object recognition; nonlinear kernel algorithm; self-supervised learning techniques; training data sets; Classification algorithms; Image classification; Image databases; Image retrieval; Information retrieval; Kernel; Semisupervised learning; Supervised learning; Support vector machine classification; Support vector machines;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 2004. Proceedings. (ICASSP '04). IEEE International Conference on

ISSN

1520-6149

Print_ISBN

0-7803-8484-9

Type

conf

DOI

10.1109/ICASSP.2004.1327147

Filename

1327147