By Topic

A strategy of classification via sparse dictionary learned by non-negative K-SVD

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Rongguo Zhang ; Key Lab. of Complex Syst. & Intell. Sci., Chinese Acad. of Sci., Beijing, China ; Chunheng Wang ; Baihua Xiao

In recent years there is a growing interest in the study of sparse representation for signals. This article extends this research into a novel model for object classification tasks. In this model, we first apply the non-negative K-SVD algorithm to learning the discriminative dictionaries using very few training samples and then represent a test image as a linear combination of atoms from these learned dictionaries based on the non-negative variation of Basis Pursuit (BP). Finally, we achieve the classification purpose by analyzing the sparse weighting coefficients. Our strategy of classification is very simple and does not ask much for the training samples. Our model is tested on two benchmark data sets Caltech-101 and UIUC-car. In both datasets, Our approach achieves the comparable performance. The idea in this paper strengthens the case for using this model in computer vision further.

Published in:

Computer Vision Workshops (ICCV Workshops), 2009 IEEE 12th International Conference on

Date of Conference:

Sept. 27 2009-Oct. 4 2009