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Statements

Subject Item

n2:RIV%2F00216224%3A14330%2F07%3A00019397%21RIV08-AV0-14330___

rdf:type

skos:Concept n11:Vysledek

dcterms:description

Similarity searching has become afundamental computational task in a variety of application areas, including multimedia information retrieval, data mining, pattern recognition, machine learning, computer vision, biomedical databases, data compression and statistical data analysis. In such environments, an exact match has little meaning, and proximity/distance (similarity/dissimilarity) concepts are typically much more fruitful for searching. In this tutorial, we review the state of the art in developing similarity search mechanisms that accept the metric space paradigm. We explain the high extensibility of the metric space approach and demonstrate its capability with examples of distance functions. The efforts to further speed up retrieval are demonstrated by a class of approximated techniques and the very recent proposals of scalable and distributed structures based on the P2P communication paradigm. Similarity searching has become afundamental computational task in a variety of application areas, including multimedia information retrieval, data mining, pattern recognition, machine learning, computer vision, biomedical databases, data compression and statistical data analysis. In such environments, an exact match has little meaning, and proximity/distance (similarity/dissimilarity) concepts are typically much more fruitful for searching. In this tutorial, we review the state of the art in developing similarity search mechanisms that accept the metric space paradigm. We explain the high extensibility of the metric space approach and demonstrate its capability with examples of distance functions. The efforts to further speed up retrieval are demonstrated by a class of approximated techniques and the very recent proposals of scalable and distributed structures based on the P2P communication paradigm. Similarity searching has become afundamental computational task in a variety of application areas, including multimedia information retrieval, data mining, pattern recognition, machine learning, computer vision, biomedical databases, data compression and statistical data analysis. In such environments, an exact match has little meaning, and proximity/distance (similarity/dissimilarity) concepts are typically much more fruitful for searching. In this tutorial, we review the state of the art in developing similarity search mechanisms that accept the metric space paradigm. We explain the high extensibility of the metric space approach and demonstrate its capability with examples of distance functions. The efforts to further speed up retrieval are demonstrated by a class of approximated techniques and the very recent proposals of scalable and distributed structures based on the P2P communication paradigm.

dcterms:title

Similarity Search: The Metric Space Approach Similarity Search: The Metric Space Approach Podobnostní hledání: Pohled metrického prostoru

skos:prefLabel

Podobnostní hledání: Pohled metrického prostoru Similarity Search: The Metric Space Approach Similarity Search: The Metric Space Approach

skos:notation

RIV/00216224:14330/07:00019397!RIV08-AV0-14330___

n3:aktivita

n14:P

n3:aktivity

P(1ET100300419), P(GP201/07/P240)

n3:dodaniDat

n8:2008

n3:domaciTvurceVysledku

n5:3540324 n5:3165647

n3:druhVysledku

n16:A

n3:duvernostUdaju

n12:S

n3:entitaPredkladatele

n10:predkladatel

n3:idSjednocenehoVysledku

449808

n3:idVysledku

RIV/00216224:14330/07:00019397

n3:jazykVysledku

n15:eng

n3:klicovaSlova

similarity search; approximate search; metric space; index structures; distributed index structure; scalability

n3:klicoveSlovo

n9:approximate%20search n9:metric%20space n9:similarity%20search n9:scalability n9:index%20structures n9:distributed%20index%20structure

n3:kodPristupu

n17:L

n3:kontrolniKodProRIV

[3517398EF4EE]

n3:mistoVydani

Seoul, Korea

n3:nosic

N/A

n3:objednatelVyzkumneZpravy

ACM

n3:obor

n13:IN

n3:pocetDomacichTvurcuVysledku

2

n3:pocetTvurcuVysledku

3

n3:projekt

n4:1ET100300419 n4:GP201%2F07%2FP240

n3:rokUplatneniVysledku

n8:2007

n3:tvurceVysledku

Amato, Giuseppe Dohnal, Vlastislav Zezula, Pavel

n3:verzeVyzkumneZpravy

ACM SAC 2007 Conference

n18:organizacniJednotka

14330