Clustering for Video Retrieval

Result code in 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>

Result on the web

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DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Clustering for Video Retrieval

Original language description

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.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

JC - Computer hardware and software

OECD FORD branch

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Project

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Continuities

Z - Vyzkumny zamer (s odkazem do CEZ)

Publication year

2009

Confidentiality

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

Article name in the collection

Data Warehousing and Knowledge Discovery

ISBN

978-3-642-03729-0

ISSN

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e-ISSN

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Number of pages

12

Pages from-to

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Publisher name

Springer Verlag

Place of publication

Heidelberg

Event location

Linz

Event date

Aug 31, 2009

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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