A probabilistic model for image representation via multiple patterns

Authors:
Jun Li;Dacheng Tao;Xuelong Li
Affiliations:
Centre for Quantum & Intelligent Systems, University of Technology, Sydney, 235 Jones Street Ultimo, Sydney, NSW 2007, Australia;Centre for Quantum & Intelligent Systems, University of Technology, Sydney, 235 Jones Street Ultimo, Sydney, NSW 2007, Australia;Center for OPTical IMagery Analysis and Learning (OPTIMAL), State Key Laboratory of Transient Optics and Photonics, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, ...
Venue:
Pattern Recognition
Year:
2012

Citing 18
Cited 1

Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection

IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonlinear component analysis as a kernel eigenvalue problem

Neural Computation
Independent component analysis: theory and applications

Independent component analysis: theory and applications
Bayesian PCA

Proceedings of the 1998 conference on Advances in neural information processing systems II
The FERET Evaluation Methodology for Face-Recognition Algorithms

IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature extraction approaches based on matrix pattern: MatPCA and MatFLDA

Pattern Recognition Letters
Rapid and brief communication: Diagonal principal component analysis for face recognition

Pattern Recognition
Rapid and brief communication: Two-dimensional locality preserving projections (2DLPP) with its application to palmprint recognition

Pattern Recognition
General Tensor Discriminant Analysis and Gabor Features for Gait Recognition

IEEE Transactions on Pattern Analysis and Machine Intelligence
Eigenfaces for recognition

Journal of Cognitive Neuroscience
Label Propagation through Linear Neighborhoods

IEEE Transactions on Knowledge and Data Engineering
Patch Alignment for Dimensionality Reduction

IEEE Transactions on Knowledge and Data Engineering
Bilinear Lanczos components for fast dimensionality reduction and feature extraction

Pattern Recognition
Manifold elastic net: a unified framework for sparse dimension reduction

Data Mining and Knowledge Discovery
Gait Components and Their Application to Gender Recognition

IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Sparse transfer learning for interactive video search reranking

ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Non-Negative Patch Alignment Framework

IEEE Transactions on Neural Networks
Manifold Regularized Discriminative Nonnegative Matrix Factorization With Fast Gradient Descent

IEEE Transactions on Image Processing

Scaling up cosine interesting pattern discovery: A depth-first method

Information Sciences: an International Journal

Quantified Score

Hi-index	0.01

Visualization

Abstract

For image analysis, an important extension to principal component analysis (PCA) is to treat an image as multiple samples, which helps alleviate the small sample size problem. Various schemes of transforming an image to multiple samples have been proposed. Although having been shown effective in practice, the schemes are mainly based on heuristics and experience. In this paper, we propose a probabilistic PCA model, in which we explicitly represent the transformation scheme and incorporate the scheme as a stochastic component of the model. Therefore fitting the model automatically learns the transformation. Moreover, the learned model allows us to distinguish regions that can be well described by the PCA model from those that need further treatment. Experiments on synthetic images and face data sets demonstrate the properties and utility of the proposed model.