Regularized mixed dimensionality and density learning in computer vision

Gloria Haro; Gregory Randall; Guillermo Sapiro

doi:10.1109/CVPR.2007.383401

Regularized mixed dimensionality and density learning in computer vision

Gloria Haro, Gregory Randall, Guillermo Sapiro

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Scopus citations

Abstract

A framework for the regularized estimation of non-uniform dimensionality and density in high dimensional data is introduced in this work. This leads to learning stratifications, that is, mixture of manifolds representing different characteristics and complexities in the data set. The basic idea relies on modeling the high dimensional sample points as a process of Poisson mixtures, with regularizing restrictions and spatial continuity constraints. Theoretical asymptotic results for the model are presented as well, The presentation of the framework is complemented with artificial and real examples showing the importance of regularized stratification learning in computer vision applications.

Original language	English (US)
Title of host publication	2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07
DOIs	https://doi.org/10.1109/CVPR.2007.383401
State	Published - 2007
Externally published	Yes
Event	2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07 - Minneapolis, MN, United States Duration: Jun 17 2007 → Jun 22 2007

Publication series

Name	Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ISSN (Print)	1063-6919

Other

Other	2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07
Country/Territory	United States
City	Minneapolis, MN
Period	6/17/07 → 6/22/07

All Science Journal Classification (ASJC) codes

Software
Computer Vision and Pattern Recognition

Access to Document

10.1109/CVPR.2007.383401

Cite this

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title = "Regularized mixed dimensionality and density learning in computer vision",

abstract = "A framework for the regularized estimation of non-uniform dimensionality and density in high dimensional data is introduced in this work. This leads to learning stratifications, that is, mixture of manifolds representing different characteristics and complexities in the data set. The basic idea relies on modeling the high dimensional sample points as a process of Poisson mixtures, with regularizing restrictions and spatial continuity constraints. Theoretical asymptotic results for the model are presented as well, The presentation of the framework is complemented with artificial and real examples showing the importance of regularized stratification learning in computer vision applications.",

author = "Gloria Haro and Gregory Randall and Guillermo Sapiro",

year = "2007",

doi = "10.1109/CVPR.2007.383401",

language = "English (US)",

isbn = "1424411807",

series = "Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition",

booktitle = "2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07",

note = "2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07 ; Conference date: 17-06-2007 Through 22-06-2007",

}

Haro, G, Randall, G & Sapiro, G 2007, Regularized mixed dimensionality and density learning in computer vision. in 2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07., 4270399, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07, Minneapolis, MN, United States, 6/17/07. https://doi.org/10.1109/CVPR.2007.383401

Regularized mixed dimensionality and density learning in computer vision. / Haro, Gloria; Randall, Gregory; Sapiro, Guillermo.
2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07. 2007. 4270399 (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AB - A framework for the regularized estimation of non-uniform dimensionality and density in high dimensional data is introduced in this work. This leads to learning stratifications, that is, mixture of manifolds representing different characteristics and complexities in the data set. The basic idea relies on modeling the high dimensional sample points as a process of Poisson mixtures, with regularizing restrictions and spatial continuity constraints. Theoretical asymptotic results for the model are presented as well, The presentation of the framework is complemented with artificial and real examples showing the importance of regularized stratification learning in computer vision applications.

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Regularized mixed dimensionality and density learning in computer vision

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