Efficient Representation of Local Geometry for Large Scale Object Retrieval
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F09%3A00163137" target="_blank" >RIV/68407700:21230/09:00163137 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Efficient Representation of Local Geometry for Large Scale Object Retrieval
Original language description
State of the art methods for image and object retrieval exploit both appearance (via visual words) and local geometry (spatial extent, relative pose). In large scale problems, memory becomes a limiting factor - local geometry is stored for each feature detected in each image and requires storage larger than the inverted file and term frequency and inverted document frequency weights together. We propose a novel method for learning discretized local geometry representation based on minimization of average reprojection error in the space of ellipses. The representation requires only 24 bits per feature without drop in performance. Additionally, we show that if the gravity vector assumption is used consistently from the feature description to spatial verification, it improves retrieval performance and decreases the memory footprint. The proposed method outperforms state of the art retrieval algorithms in a standard image retrieval benchmark.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
JD - Use of computers, robotics and its application
OECD FORD branch
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Result continuities
Project
<a href="/en/project/GA102%2F07%2F1317" target="_blank" >GA102/07/1317: Methods for Visual Recognition of Large Collections of Non-rigid Objects</a><br>
Continuities
R - Projekt Ramcoveho programu EK
Others
Publication year
2009
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
CVPR 2009: Proceedings of the 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ISBN
978-1-4244-3991-1
ISSN
1063-6919
e-ISSN
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Number of pages
8
Pages from-to
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Publisher name
Omnipress
Place of publication
Madison
Event location
Fontainebleau Resort, Miami Beach, Florida
Event date
Jun 20, 2009
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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