Unsupervised Discovery of Co-occurrence in Sparse High Dimensional Data

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F10%3A00175499" target="_blank" >RIV/68407700:21230/10:00175499 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Unsupervised Discovery of Co-occurrence in Sparse High Dimensional Data
Original language description
An efficient min-Hash based algorithm for discovery of dependencies in sparse high-dimensional data is presented. The dependencies are represented by sets of features cooccurring with high probability and are called co-ocsets. Sparse high dimensional descriptors, such as bag of words, have been proven very effective in the domain of image retrieval. To maintain high efficiency even for very large data collection, features are assumed independent. We show experimentally that co-ocsets are not rare, i.e.the independence assumption is often violated, and that they may ruin retrieval performance if present in the query image. Two methods for managing co-ocsets in such cases are proposed. Both methods significantly outperform the state-of-the-art in imageretrieval, one is also significantly faster.
Czech name
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Czech description
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Project
<a href="/en/project/GP102%2F09%2FP423" target="_blank" >GP102/09/P423: High-dimensional Similarity Measures for Web Scale Object and Category Search</a><br>
Continuities
S - Specificky vyzkum na vysokych skolach<br>R - Projekt Ramcoveho programu EK

Publication year
2010
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
CVPR 2010: Proceedings of the 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ISBN
978-1-4244-6984-0
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
San Francisco
Event date
Jun 13, 2010
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000287417503060

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