Analyzing and dynamically indexing the query set

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F14%3A10291426" target="_blank" >RIV/00216208:11320/14:10291426 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1016/j.is.2013.05.010" target="_blank" >http://dx.doi.org/10.1016/j.is.2013.05.010</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.is.2013.05.010" target="_blank" >10.1016/j.is.2013.05.010</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Analyzing and dynamically indexing the query set

Popis výsledku v původním jazyce

Most of the current metric indexes focus on indexing the collection of reference. In this work we study the problem of indexing the query set by exploiting some property that query objects may have. Thereafter, we present the Snake Table, which is an index structure designed for supporting streams of k-NN searches within a content-based similarity search framework. The index is created and updated in the online phase while resolving the queries, thus it does not need a preprocessing step. This index isintended to be used when the stream of query objects fits a snake distribution, that is, when the distance between two consecutive query objects is small. In particular, this kind of distribution is present in content-based video retrieval systems, imageclassification based on local descriptors, rotation-invariant shape matching, and others. We show that the Snake Table improves the efficiency of k-NN searches in these systems, avoiding the building of a static index in the offline phas

Název v anglickém jazyce

Analyzing and dynamically indexing the query set

Popis výsledku anglicky

Most of the current metric indexes focus on indexing the collection of reference. In this work we study the problem of indexing the query set by exploiting some property that query objects may have. Thereafter, we present the Snake Table, which is an index structure designed for supporting streams of k-NN searches within a content-based similarity search framework. The index is created and updated in the online phase while resolving the queries, thus it does not need a preprocessing step. This index isintended to be used when the stream of query objects fits a snake distribution, that is, when the distance between two consecutive query objects is small. In particular, this kind of distribution is present in content-based video retrieval systems, imageclassification based on local descriptors, rotation-invariant shape matching, and others. We show that the Snake Table improves the efficiency of k-NN searches in these systems, avoiding the building of a static index in the offline phas

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

<a href="/cs/project/GAP202%2F11%2F0968" target="_blank" >GAP202/11/0968: Podobnostní nemetrické vyhledávání v rozsáhlých komplexních databázích</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2014

Kód důvěrnosti údajů

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

Název periodika

Information Systems

ISSN

0306-4379

e-ISSN

—

Svazek periodika

45

Číslo periodika v rámci svazku

September

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

11

Strana od-do

37-47

Kód UT WoS článku

000338613900004

EID výsledku v databázi Scopus

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