Tensor Discriminant Analysis for View-based Object Recognition

Authors:
Yong Wang;Shaogang Gong
Affiliations:
Queen Mary, University of London, E1 4NS, United Kingdom;Queen Mary, University of London, E1 4NS, United Kingdom
Venue:
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Year:
2006

Citing 0
Cited 6

Uncorrelated multilinear discriminant analysis with regularization and aggregation for tensor object recognition

IEEE Transactions on Neural Networks
Uncorrelated multilinear principal component analysis for unsupervised multilinear subspace learning

IEEE Transactions on Neural Networks
Distance approximating dimension reduction of Riemannian manifolds

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Multilinear tensor supervised neighborhood embedding analysis for view-based object recognition

PCM'10 Proceedings of the 11th Pacific Rim conference on Advances in multimedia information processing: Part I
A survey of multilinear subspace learning for tensor data

Pattern Recognition
Simple object recognition based on spatial relations and visual features represented using irregular pyramids

Multimedia Tools and Applications

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we use a general Mth order tensor discriminant analysis approach [11] for view based object recognition. This method is an extension of the 2D image coding technique [10] to general Mth order tensors for discriminant analysis, and has good convergence property. We demonstrate the performance advantages of this approach over existing techniques using experiments on the COIL-100 and the ETH-80 datasets. Specifically, our experimental results on ETH-80 show the particular strength of this tensor discriminant analysis method when only a small number of training samples with big intra-class variation are available.