Self-supervised learning based on discriminative nonlinear features for image classification

Authors:
Qi Tian;Ying Wu;Jie Yu;Thomas S. Huang
Affiliations:
Department of Computer Science, University of Texas at San Antonio, 6900 North Loop 1604 West, San Antonio, TX 78249, USA;Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL 60208, USA;Department of Computer Science, University of Texas at San Antonio, 6900 North Loop 1604 West, San Antonio, TX 78249, USA;Beckman Institute, University of Illinois, 405 N. Mathews Ave, Urbana, IL 61801, USA
Venue:
Pattern Recognition
Year:
2005

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Abstract

For learning-based tasks such as image classification, the feature dimension is usually very high. The learning is afflicted by the curse of dimensionality as the search space grows exponentially with the dimension. Discriminant-EM (DEM) proposed a framework by applying self-supervised learning in a discriminating subspace. This paper extends the linear DEM to a nonlinear kernel algorithm, Kernel DEM (KDEM), and evaluates KDEM extensively on benchmark image databases and synthetic data. Various comparisons with other state-of-the-art learning techniques are investigated for several tasks of image classification. Extensive results show the effectiveness of our approach.