All

What are you looking for?

All
Projects
Results
Organizations

Quick search

  • Projects supported by TA ČR
  • Excellent projects
  • Projects with the highest public support
  • Current projects

Smart search

  • That is how I find a specific +word
  • That is how I leave the -word out of the results
  • “That is how I can find the whole phrase”

Nonmetric Similarity Search Problems in Very Large Collections

The result's identifiers

  • Result code in 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>

  • Result on the web

    <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>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Nonmetric Similarity Search Problems in Very Large Collections

  • Original language description

    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.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

    IN - Informatics

  • OECD FORD branch

Result continuities

  • Project

    <a href="/en/project/GA201%2F09%2F0683" target="_blank" >GA201/09/0683: Similarity searching in very large multimedia databases</a><br>

  • Continuities

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

Others

  • Publication year

    2011

  • Confidentiality

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

Data specific for result type

  • Article name in the collection

    IEEE 27th International Conference on Data Engineering (ICDE)

  • ISBN

    978-1-4244-8959-6

  • ISSN

    1063-6382

  • e-ISSN

  • Number of pages

    4

  • Pages from-to

    1362-1365

  • Publisher name

    IEEE

  • Place of publication

    Neuveden

  • Event location

    Hannover, Germany

  • Event date

    Apr 11, 2011

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article

    000295216600134