By Topic

Neighborhood Preserving Non-negative Tensor Factorization for image representation

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
$33 $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)
Yu-Xiong Wang ; Department of Electronic Engineering, Tsinghua University, Tsinghua National Laboratory for Information Science and Technology, Beijing 100084, China ; Liang-Yan Gui ; Yu-Jin Zhang

Non-negative Matrix Factorization (NMF) has become a powerful tool for image representation due to its enhanced semantic interpretability under non-negativity. Unfortunately, two types of neighborhood information essential to representation are lost in NMF. For individual image, the local structure information is missing in the vectorization, which can then be avoided by Non-negative Tensor Factorization (NTF). For image data points, they often reside on a low dimensional submanifold embedded in a high dimensional ambient space. NMF and NTF are incapable of encoding the local geometrical information, which can nevertheless be resuscitated by manifold learning. To simultaneously model both of the neighborhood relationship within and among image data, this paper proposes a novel algorithm called Neighborhood Preserving Non-negative Tensor Factorization (NPNTF) by incorporating locally linear embedding regularization into tensor factorization. Experimental results on image clustering show the superior performance of NPNTF with more natural and discriminating representation ability.

Published in:

2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Date of Conference:

25-30 March 2012