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IVC
2007
184views more  IVC 2007»
13 years 7 months ago
Image distance functions for manifold learning
Many natural image sets are samples of a low-dimensional manifold in the space of all possible images. When the image data set is not a linear combination of a small number of bas...
Richard Souvenir, Robert Pless
PAKDD
2005
ACM
168views Data Mining» more  PAKDD 2005»
14 years 25 days ago
Adaptive Nonlinear Auto-Associative Modeling Through Manifold Learning
We propose adaptive nonlinear auto-associative modeling (ANAM) based on Locally Linear Embedding algorithm (LLE) for learning intrinsic principal features of each concept separatel...
Junping Zhang, Stan Z. Li
ICPR
2002
IEEE
14 years 8 months ago
Manifold Pursuit: A New Approach to Appearance Based Recognition
Manifold Pursuit (MP) extends Principal Component Analysis to be invariant to a desired group of image-plane transformations of an ensemble of un-aligned images. We derive a simpl...
Amnon Shashua, Anat Levin, Shai Avidan
ICIP
2007
IEEE
14 years 9 months ago
Projection onto a Shape Manifold for Image Segmentation with Prior
Image segmentation with shape priors has received a lot of attention over the past years. Most existing work focuses on a linearized shape space with small deformation modes aroun...
Florent Ségonne, Patrick Etyngier, Renaud K...
ICCV
2007
IEEE
14 years 1 months ago
Laplacian PCA and Its Applications
Dimensionality reduction plays a fundamental role in data processing, for which principal component analysis (PCA) is widely used. In this paper, we develop the Laplacian PCA (LPC...
Deli Zhao, Zhouchen Lin, Xiaoou Tang