Unsupervised Discovery of Co-occurrence in Sparse High Dimensional Data

Kód výsledku v 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>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Unsupervised Discovery of Co-occurrence in Sparse High Dimensional Data

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Unsupervised Discovery of Co-occurrence in Sparse High Dimensional Data

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

JD - Využití počítačů, robotika a její aplikace

OECD FORD obor

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Projekt

<a href="/cs/project/GP102%2F09%2FP423" target="_blank" >GP102/09/P423: Vysokodimenzionální míry podobnosti pro vyhledávání objektů a tříd ve velikých databázích obrázků</a><br>

Návaznosti

S - Specificky vyzkum na vysokych skolach<br>R - Projekt Ramcoveho programu EK

Rok uplatnění

2010

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

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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Počet stran výsledku

8

Strana od-do

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Název nakladatele

Omnipress

Místo vydání

Madison

Místo konání akce

San Francisco

Datum konání akce

13. 6. 2010

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000287417503060