Nonmetric Similarity Search Problems in Very Large Collections

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F11%3A10048617" target="_blank" >RIV/00216208:11320/11:10048617 - isvavai.cz</a>

Výsledek na webu

<a href="http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5767955&tag=1" target="_blank" >http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5767955&tag=1</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ICDE.2011.5767955" target="_blank" >10.1109/ICDE.2011.5767955</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Nonmetric Similarity Search Problems in Very Large Collections

Popis výsledku v původním jazyce

Similarity search is a fundamental problem in many disciplines like multimedia databases, data mining, bioinformatics, computer vision, and pattern recognition, among others. The standard approach for implementing similarity search is to define a dissimilarity measure that satisfies the properties of a metric (strict positiveness, symmetry, and the triangle inequality), and then use it to query for similar objects in large data collections. The advantage of this approach is that there are many index structures (so-called metric access methods) that can be used to efficiently perform the queries. However, a recent survey [91] has shown that similarity measures not holding the metric properties have been widely used for content-based retrieval, because these (usually) more complex similarity measures are more effective and give better results.

Název v anglickém jazyce

Nonmetric Similarity Search Problems in Very Large Collections

Popis výsledku anglicky

Similarity search is a fundamental problem in many disciplines like multimedia databases, data mining, bioinformatics, computer vision, and pattern recognition, among others. The standard approach for implementing similarity search is to define a dissimilarity measure that satisfies the properties of a metric (strict positiveness, symmetry, and the triangle inequality), and then use it to query for similar objects in large data collections. The advantage of this approach is that there are many index structures (so-called metric access methods) that can be used to efficiently perform the queries. However, a recent survey [91] has shown that similarity measures not holding the metric properties have been widely used for content-based retrieval, because these (usually) more complex similarity measures are more effective and give better results.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

—

Projekt

<a href="/cs/project/GA201%2F09%2F0683" target="_blank" >GA201/09/0683: Vyhledávání v rozsáhlých multimediálních databázích</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2011

Kód důvěrnosti údajů

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

Název statě ve sborníku

IEEE 27th International Conference on Data Engineering (ICDE)

ISBN

978-1-4244-8959-6

ISSN

1063-6382

e-ISSN

—

Počet stran výsledku

4

Strana od-do

1362-1365

Název nakladatele

IEEE

Místo vydání

Neuveden

Místo konání akce

Hannover, Germany

Datum konání akce

11. 4. 2011

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000295216600134