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Description
  • 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 is intended 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, image classification 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 phase.
  • 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 is intended 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, image classification 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 phase. (en)
Title
  • Analyzing and dynamically indexing the query set
  • Analyzing and dynamically indexing the query set (en)
skos:prefLabel
  • Analyzing and dynamically indexing the query set
  • Analyzing and dynamically indexing the query set (en)
skos:notation
  • RIV/00216208:11320/14:10291426!RIV15-GA0-11320___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(GAP202/11/0968)
http://linked.open...iv/cisloPeriodika
  • September
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 3216
http://linked.open...ai/riv/idVysledku
  • RIV/00216208:11320/14:10291426
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • Content-based multimedia retrieval; Multimedia information retrieval; Metric indexing; Similarity search (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...odStatuVydavatele
  • GB - Spojené království Velké Británie a Severního Irska
http://linked.open...ontrolniKodProRIV
  • [EDA007CF86D2]
http://linked.open...i/riv/nazevZdroje
  • Information Systems
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...v/svazekPeriodika
  • 45
http://linked.open...iv/tvurceVysledku
  • Skopal, Tomáš
  • Bustos, Benjamin
  • Barrios, Juan Manuel
http://linked.open...ain/vavai/riv/wos
  • 000338613900004
issn
  • 0306-4379
number of pages
http://bibframe.org/vocab/doi
  • 10.1016/j.is.2013.05.010
http://localhost/t...ganizacniJednotka
  • 11320
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