MPCA: Multilinear Principal Component Analysis of Tensor Objects
Top Cited Papers
- 14 January 2008
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Neural Networks
- Vol. 19 (1), 18-39
- https://doi.org/10.1109/tnn.2007.901277
Abstract
This paper introduces a multilinear principal component analysis (MPCA) framework for tensor object feature extraction. Objects of interest in many computer vision and pattern recognition applications, such as 2D/3D images and video sequences are naturally described as tensors or multilinear arrays. The proposed framework performs feature extraction by determining a multilinear projection that captures most of the original tensorial input variation. The solution is iterative in nature and it proceeds by decomposing the original problem to a series of multiple projection subproblems. As part of this work, methods for subspace dimensionality determination are proposed and analyzed. It is shown that the MPCA framework discussed in this work supplants existing heterogeneous solutions such as the classical principal component analysis (PCA) and its 2D variant (2D PCA). Finally, a tensor object recognition system is proposed with the introduction of a discriminative tensor feature selection mechanism and a novel classification strategy, and applied to the problem of gait recognition. Results presented here indicate MPCA's utility as a feature extraction tool. It is shown that even without a fully optimized design, an MPCA-based gait recognition module achieves highly competitive performance and compares favorably to the state-of-the-art gait recognizers.Keywords
This publication has 31 references indexed in Scilit:
- Multilinear Discriminant Analysis for Face RecognitionIEEE Transactions on Image Processing, 2006
- Human Gait Recognition With Matrix RepresentationIEEE Transactions on Circuits and Systems for Video Technology, 2006
- 3D face detection using curvature analysisPattern Recognition, 2006
- Discriminant Analysis with Tensor RepresentationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- Selecting discriminant eigenfaces for face recognitionPattern Recognition Letters, 2005
- Computational and Performance Aspects of PCA-Based Face-Recognition AlgorithmsPerception, 2001
- Eigenfaces vs. Fisherfaces: recognition using class specific linear projectionIeee Transactions On Pattern Analysis and Machine Intelligence, 1997
- Eigenfaces for RecognitionJournal of Cognitive Neuroscience, 1991
- Matrix AnalysisPublished by Cambridge University Press (CUP) ,1985
- Principal component analysis of three-mode data by means of alternating least squares algorithmsPsychometrika, 1980