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Geometric Latent Dirichlet Allocation on a matching graph for large-scale image datasets

Abstract:

Given a large-scale collection of images our aim is to efficiently associate images which contain the same entity, for example a building or object, and to discover the significant entities. To achieve this, we introduce the Geometric Latent Dirichlet Allocation (gLDA) model for unsupervised discovery of particular objects in unordered image collections. This explicitly represents images as mixtures of particular objects or facades, and builds rich latent topic models which incorporate the...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1007/s11263-010-0363-5

Authors


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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Research group:
Visual Geometry Group
Role:
Author
More by this author
Institution:
INRIA - Willow Project, Laboratoire d'Informatique de l'Ecole Normale Supérieure (CNRS/ENS/INRIA UMR 8548), Paris
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
Royal Academy of Engineering More from this funder
Engineering and Physical Sciences Research Council More from this funder
Publisher:
Springer Publisher's website
Journal:
International Journal of Computer Vision Journal website
Volume:
95
Issue:
2
Pages:
138-153
Publication date:
2011-01-01
DOI:
EISSN:
1573-1405
ISSN:
0920-5691
Language:
English
Keywords:
Subjects:
UUID:
uuid:5c0a534d-1554-45cc-9133-21ab9a102199
Local pid:
ora:5836
Deposit date:
2011-10-31

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