Feature extraction from faces using deformable templates
International Journal of Computer Vision
Inducing Features of Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Minimax entropy principle and its application to texture modeling
Neural Computation
Prior Learning and Gibbs Reaction-Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stochastic models for generic images
Quarterly of Applied Mathematics
The Nonlinear Statistics of High-Contrast Patches in Natural Images
International Journal of Computer Vision - Special Issue on Computational Vision at Brown University
Recognizing Surfaces Using Three-Dimensional Textons
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Transformed Component Analysis: Joint Estimation of Spatial Transformations and Image Components
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
International Journal of Computer Vision - Special Issue on Texture Analysis and Synthesis
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Fields of Experts: A Framework for Learning Image Priors
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Information theory and statistics: a tutorial
Communications and Information Theory
Primal sketch: Integrating structure and texture
Computer Vision and Image Understanding
A stochastic grammar of images
Foundations and Trends® in Computer Graphics and Vision
Perceptual Scale-Space and Its Applications
International Journal of Computer Vision
Mapping natural image patches by explicit and implicit manifolds
Mapping natural image patches by explicit and implicit manifolds
Learning Active Basis Model for Object Detection and Recognition
International Journal of Computer Vision
Segmentation of Multivariate Mixed Data via Lossy Data Coding and Compression
IEEE Transactions on Pattern Analysis and Machine Intelligence
A local spectral distribution approach to face recognition
Computer Vision and Image Understanding
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Natural images have a vast amount of visual patterns distributed in a wide spectrum of subspaces of varying complexities and dimensions. Understanding the characteristics of these subspaces and their compositional structures is of fundamental importance for pattern modeling, learning and recognition. In this paper, we start with small image patches and define two types of atomic subspaces: explicit manifolds of low dimensions for structural primitives and implicit manifolds of high dimensions for stochastic textures. Then we present an information theoretical learning framework that derives common models for these manifolds through information projection, and study a manifold pursuit algorithm that clusters image patches into those atomic subspaces and ranks them according to their information gains. We further show how those atomic subspaces change over an image scaling process and how they are composed to form larger and more complex image patterns. Finally, we integrate the implicit and explicit manifolds to form a primal sketch model as a generic representation in early vision and to generate a hybrid image template representation for object category recognition in high level vision. The study of the mathematical structures in the image space sheds lights on some basic questions in human vision, such as atomic elements in visual perception, the perceptual metrics in various manifolds, and the perceptual transitions over image scales. This paper is based on the J.K. Aggarwal Prize lecture by the first author at the International Conference on Pattern Recognition, Tempa, FL. 2008.