Aggregative query generation

Reede Ren; Martin Halvey; Joemon M. Jose

doi:10.1109/ICME.2009.5202628

Aggregative query generation

Reede Ren, Martin Halvey, Joemon M. Jose

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

This paper proposes an aggregative query generation which exploits a media document representation called feature term to create a query from multiple media examples, e.g. images. A feature term denotes an interval of one media feature dimension, such as a bin in colour histogram. This approach (1) can easily accumulate features from multiple query examples to generate an efficient query; (2) enables the exploration of text-based retrieval models for multimedia retrieval. Two criteria, minimised chi2 and maximised entropy, are proposed to optimise feature term selection.

Original language	English
Title of host publication	Proceedings of the IEEE International Conference on Multimedia and Expo
Publisher	IEEE
Pages	850-853
Number of pages	4
ISBN (Print)	9781424442904
DOIs	https://doi.org/10.1109/ICME.2009.5202628
Publication status	Published - 2009

Keywords

query generation
image retrieval
multimedia

Documents and Links

10.1109/ICME.2009.5202628

Cite this

@inproceedings{51d458d3e27048f09ae7797c27afe8fa,

title = "Aggregative query generation",

abstract = "This paper proposes an aggregative query generation which exploits a media document representation called feature term to create a query from multiple media examples, e.g. images. A feature term denotes an interval of one media feature dimension, such as a bin in colour histogram. This approach (1) can easily accumulate features from multiple query examples to generate an efficient query; (2) enables the exploration of text-based retrieval models for multimedia retrieval. Two criteria, minimised chi2 and maximised entropy, are proposed to optimise feature term selection. ",

keywords = "query generation, image retrieval, multimedia",

author = "Reede Ren and Martin Halvey and Jose, {Joemon M.}",

year = "2009",

doi = "10.1109/ICME.2009.5202628",

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booktitle = "Proceedings of the IEEE International Conference on Multimedia and Expo",

publisher = "IEEE",

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T1 - Aggregative query generation

AU - Ren, Reede

AU - Halvey, Martin

AU - Jose, Joemon M.

PY - 2009

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N2 - This paper proposes an aggregative query generation which exploits a media document representation called feature term to create a query from multiple media examples, e.g. images. A feature term denotes an interval of one media feature dimension, such as a bin in colour histogram. This approach (1) can easily accumulate features from multiple query examples to generate an efficient query; (2) enables the exploration of text-based retrieval models for multimedia retrieval. Two criteria, minimised chi2 and maximised entropy, are proposed to optimise feature term selection.

AB - This paper proposes an aggregative query generation which exploits a media document representation called feature term to create a query from multiple media examples, e.g. images. A feature term denotes an interval of one media feature dimension, such as a bin in colour histogram. This approach (1) can easily accumulate features from multiple query examples to generate an efficient query; (2) enables the exploration of text-based retrieval models for multimedia retrieval. Two criteria, minimised chi2 and maximised entropy, are proposed to optimise feature term selection.

KW - query generation

KW - image retrieval

KW - multimedia

U2 - 10.1109/ICME.2009.5202628

DO - 10.1109/ICME.2009.5202628

M3 - Conference contribution

SN - 9781424442904

SP - 850

EP - 853

BT - Proceedings of the IEEE International Conference on Multimedia and Expo

PB - IEEE

ER -

Aggregative query generation

Abstract

Keywords

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Cite this