Journal article
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...
Expand abstract
- Publication status:
- Published
- Peer review status:
- Peer reviewed
Actions
Access Document
- Publisher copy:
- 10.1007/s11263-010-0363-5
Why is the content I wish to access not available via ORA?
Bibliographic data (the information relating to research outputs) and full-text items (e.g. articles, theses, reports, etc.) arrive in ORA from several different sources. Unfortunately we are not able to make available the full-text for every research output.
Please contact the ORA team (
) if you have queries regarding unavailable content OR if you are aware of a full-text copy we can make available.
) if you have queries regarding unavailable content OR if you are aware of a full-text copy we can make available.
Content may be unavailable for the following four reasons
-
Version unsuitable
-
We have not obtained a suitable full-text for a given research output. See this page for more information.
-
Recently completed
-
Sometimes content is held in ORA but is unavailable for a fixed period of time to comply with the policies and wishes of rights holders.
-
Permissions
-
All content made available in ORA should comply with relevant rights, such as copyright. See this page for more information.
-
Clearance
-
Some thesis volumes scanned as part of the digitisation scheme funded by Dr Leonard Polonsky are currently unavailable due to sensitive material or uncleared third-party copyright content. We are attempting to contact authors whose theses are affected.
Alternative access to the full-text
You may be able to access the full-text directly from the publisher's website using the 'Publisher Copy' link in the 'Links & Downloads' box from a research output's ORA record page. This method may require an institutional or individual subscription to the journal/resource.
Authors
Funding
Royal Academy of Engineering
More from this funder
Engineering and Physical Sciences Research Council
More from this funder
Microsoft
More from this funder
Bibliographic Details
- 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
Item Description
- Language:
- English
- Keywords:
- Subjects:
- UUID:
-
uuid:5c0a534d-1554-45cc-9133-21ab9a102199
- Local pid:
- ora:5836
- Deposit date:
- 2011-10-31
Related Items
Terms of use
- Copyright holder:
- Springer Science + Business Media, LLC
- Copyright date:
- 2010
- Notes:
- The full-text of this article is not currently available in ORA, but the original publication is available at springerlink.com (which you may be able to access via the publisher copy link on this record page).
If you are the owner of this record, you can report an update to it here: Report update to this record