Learning nonlinear image manifolds by global alignment of local linear models.

Full Abstract

Appearance-based methods, based on statistical models of the pixel values in an image (region) rather than geometrical object models, are increasingly popular in computer vision. In many applications, the number of degrees of freedom (DOF) in the image generating process is much lower than the number of pixels in the image. If there is a smooth function that maps the DOF to the pixel values, then the images are confined to a low-dimensional manifold embedded in the image space. We propose a method based on probabilistic mixtures of factor analyzers to (1) model the density of images sampled from such manifolds and (2) recover global parameterizations of the manifold. A globally nonlinear probabilistic two-way mapping between coordinates on the manifold and images is obtained by combining several, locally valid, linear mappings. We propose a parameter estimation scheme that improves upon an existing scheme and experimentally compare the presented approach to self-organizing maps, generative topographic mapping, and mixtures of factor analyzers. In addition, we show that the approach also applies to finding mappings between different embeddings of the same manifold.

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Author information

Author/s: Verbeek, Jakob (J);

Affiliation: GRAVIR-INRIA, 655 avenue de l'Europe, 38330 Montbonnot, France. verbeek(-atsign-)inrialpes.fr

Journal and publication information

Publication Type: Journal Article

Journal: IEEE transactions on pattern analysis and machine intelligence (IEEE Trans Pattern Anal Mach Intell), published in United States. (Language: eng)

Reference: 2006-Aug; vol 28 (issue 8) : pp 1236-50

Dates: Created 2006/08/04; Completed 2006/09/05;

PMID: 16886860, status: MEDLINE (last retrieval date: 12/26/2008)

Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.

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MeSH headings (categories)

This article was linked to the MESH Headings shown below.

Algorithms
Artificial Intelligence
Computer Simulation
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods

Imaging, Three-Dimensional - methods
Information Storage and Retrieval - methods
Linear Models
Nonlinear Dynamics
Pattern Recognition, Automated - methods

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