Clustering for Video Retrieval

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F09%3APU82627" target="_blank" >RIV/00216305:26230/09:PU82627 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Clustering for Video Retrieval

Popis výsledku v původním jazyce

The paper deals with an application of clustering we used as one of data reduction methods included in processing huge amount of video data provided for TRECVid evaluations. The problem we solved by means of clustering was to partition the local featuredescriptors space so that thousands of partitions represent visual words, which may be effectively employed in video retrieval using classical information retrieval techniques. It has proved that well-known algorithms as K-means do not work well in thistask or their computational complexity is too high. Therefore we developed a simple clustering method (referred to as MLD) that partitions the high-dimensional feature space incrementally in one to two database scans. The paper describes the problem of video retrieval and the role of clustering in the process, the MLD method and experiments focused on comparison with other clustering methods in the video retrieval application context.

Název v anglickém jazyce

Clustering for Video Retrieval

Popis výsledku anglicky

The paper deals with an application of clustering we used as one of data reduction methods included in processing huge amount of video data provided for TRECVid evaluations. The problem we solved by means of clustering was to partition the local featuredescriptors space so that thousands of partitions represent visual words, which may be effectively employed in video retrieval using classical information retrieval techniques. It has proved that well-known algorithms as K-means do not work well in thistask or their computational complexity is too high. Therefore we developed a simple clustering method (referred to as MLD) that partitions the high-dimensional feature space incrementally in one to two database scans. The paper describes the problem of video retrieval and the role of clustering in the process, the MLD method and experiments focused on comparison with other clustering methods in the video retrieval application context.

Druh

D - Stať ve sborníku

CEP obor

JC - Počítačový hardware a software

OECD FORD obor

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Projekt

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Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2009

Kód důvěrnosti údajů

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

Název statě ve sborníku

Data Warehousing and Knowledge Discovery

ISBN

978-3-642-03729-0

ISSN

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e-ISSN

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Počet stran výsledku

12

Strana od-do

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Název nakladatele

Springer Verlag

Místo vydání

Heidelberg

Místo konání akce

Linz

Datum konání akce

31. 8. 2009

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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