Discrete cosine transform: algorithms, advantages, applications
Discrete cosine transform: algorithms, advantages, applications
Active shape models—their training and application
Computer Vision and Image Understanding
Visual learning and recognition of 3-D objects from appearance
International Journal of Computer Vision
What Is the Set of Images of an Object Under All Possible Illumination Conditions?
International Journal of Computer Vision
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Atomic Decomposition by Basis Pursuit
SIAM Journal on Scientific Computing
Algorithm 805: computation and uses of the semidiscrete matrix decomposition
ACM Transactions on Mathematical Software (TOMS)
Multi-Frame Correspondence Estimation Using Subspace Constraints
International Journal of Computer Vision
Lambertian Reflectance and Linear Subspaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Approximation of functions over redundant dictionaries using coherence
SODA '03 Proceedings of the fourteenth annual ACM-SIAM symposium on Discrete algorithms
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
EigenTracking: Robust Matching and Tracking of Articulated Objects Using a View-Based Representation
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Fast template matching using bounded partial correlation
Machine Vision and Applications
Manifold Pursuit: A New Approach to Appearance Based Recognition
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Detecting Pedestrians Using Patterns of Motion and Appearance
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Real Time Pattern Matching Using Projection Kernels
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Real Time Pattern Matching Using Projection Kernels
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Non-negative Matrix Factorization with Sparseness Constraints
The Journal of Machine Learning Research
Handbook of Face Recognition
Digital Image Processing (3rd Edition)
Digital Image Processing (3rd Edition)
Sparse Image Coding Using a 3D Non-Negative Tensor Factorization
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Efficient Visual Event Detection Using Volumetric Features
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
A Multi-Scale Hybrid Linear Model for Lossy Image Representation
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Non-Orthogonal Binary Subspace and Its Applications in Computer Vision
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Joint Haar-like Features for Face Detection
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Learning Non-Negative Sparse Image Codes by Convex Programming
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Generalized Principal Component Analysis (GPCA)
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visual tracking and recognition using probabilistic appearance manifolds
Computer Vision and Image Understanding
A swapping-based refinement of orthogonal matching pursuit strategies
Signal Processing - Sparse approximations in signal and image processing
Image Denoising Via Learned Dictionaries and Sparse representation
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computation of the Fast Walsh-Fourier Transform
IEEE Transactions on Computers
A Projection Pursuit Algorithm for Exploratory Data Analysis
IEEE Transactions on Computers
Fast variable window for stereo correspondence using integral images
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Sparse component analysis and blind source separation of underdetermined mixtures
IEEE Transactions on Neural Networks
Binary sparse nonnegative matrix factorization
IEEE Transactions on Circuits and Systems for Video Technology
To obtain orthogonal feature extraction using training data selection
Proceedings of the 18th ACM conference on Information and knowledge management
Fast Haar transform based feature extraction for face representation and recognition
IEEE Transactions on Information Forensics and Security
Stochastic orthogonal and nonorthogonal subspace basis pursuit
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Orientation distance-based discriminative feature extraction for multi-class classification
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Discriminative nonorthogonal binary subspace tracking
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
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Efficient and compact representation of images is a fundamental problem in computer vision. In this paper, we propose methods that use Haar-like binary box functions to represent a single image or a set of images. A desirable property of these box functions is that their inner product operation with an image can be computed very efficiently. We propose two closely related novel subspace methods to model images: the non-orthogonal binary subspace (NBS) method and binary principal component analysis (B-PCA) algorithm. NBS is spanned directly by binary box functions and can be used for image representation, fast template matching and many other vision applications. B-PCA is a structure subspace that inherits the merits of both NBS (fast computation) and PCA (modeling data structure information). B-PCA base vectors are obtained by a novel PCA guided NBS method. We also show that BPCA base vectors are nearly orthogonal to each other. As a result, in the non-orthogonal vector decomposition process, the computationally intensive pseudo-inverse projection operator can be approximated by the direct dot product without causing significant distance distortion. Experiments on real image datasets show promising performance in image matching, reconstruction and recognition tasks with significant speed improvement.