Applying information foraging theory to understand user interaction with content-based image retrieval

Haiming Liu; Paul Mulholland; Dawei Song; Victoria Uren; Stefan Rüger

doi:10.1145/1840784.1840805

Applying information foraging theory to understand user interaction with content-based image retrieval

Haiming Liu
, Paul Mulholland
, Dawei Song
, Victoria Uren
, Stefan Rüger

Institute for Research in Applicable Computing (IRAC)

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

24 Citations (Scopus)

Abstract

The paper proposes an ISE (Information goal, Search strategy, Evaluation threshold) user classification model based on Information Foraging Theory for understanding user interaction with content-based image retrieval (CBIR). The proposed model is verified by a multiple linear regression analysis based on 50 users' interaction features collected from a task-based user study of interactive CBIR systems. To our best knowledge, this is the first principled user classification model in CBIR verified by a formal and systematic qualitative analysis of extensive user interaction data.

Original language	English
Title of host publication	nan
Publisher	Association for Computing Machinery
ISBN (Electronic)	9781450302470
ISBN (Print)	9781450302470
DOIs	https://doi.org/10.1145/1840784.1840805
Publication status	Published - 1 Jan 2010
Event	3rd Information Interaction in Context Symposium - Duration: 1 Jan 2010 → …

Conference

Conference	3rd Information Interaction in Context Symposium
Period	1/01/10 → …
Other	3rd Information Interaction in Context Symposium

Keywords

Information Foraging Theory

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10.1145/1840784.1840805

http://portal.acm.org/citation.cfm%3Fdoid%3D1840784.1840805

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title = "Applying information foraging theory to understand user interaction with content-based image retrieval",

abstract = "The paper proposes an ISE (Information goal, Search strategy, Evaluation threshold) user classification model based on Information Foraging Theory for understanding user interaction with content-based image retrieval (CBIR). The proposed model is verified by a multiple linear regression analysis based on 50 users' interaction features collected from a task-based user study of interactive CBIR systems. To our best knowledge, this is the first principled user classification model in CBIR verified by a formal and systematic qualitative analysis of extensive user interaction data.",

keywords = "Information Foraging Theory",

author = "Haiming Liu and Paul Mulholland and Dawei Song and Victoria Uren and Stefan R{\"u}ger",

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AU - Liu, Haiming

AU - Mulholland, Paul

AU - Song, Dawei

AU - Uren, Victoria

AU - Rüger, Stefan

PY - 2010/1/1

Y1 - 2010/1/1

N2 - The paper proposes an ISE (Information goal, Search strategy, Evaluation threshold) user classification model based on Information Foraging Theory for understanding user interaction with content-based image retrieval (CBIR). The proposed model is verified by a multiple linear regression analysis based on 50 users' interaction features collected from a task-based user study of interactive CBIR systems. To our best knowledge, this is the first principled user classification model in CBIR verified by a formal and systematic qualitative analysis of extensive user interaction data.

AB - The paper proposes an ISE (Information goal, Search strategy, Evaluation threshold) user classification model based on Information Foraging Theory for understanding user interaction with content-based image retrieval (CBIR). The proposed model is verified by a multiple linear regression analysis based on 50 users' interaction features collected from a task-based user study of interactive CBIR systems. To our best knowledge, this is the first principled user classification model in CBIR verified by a formal and systematic qualitative analysis of extensive user interaction data.

KW - Information Foraging Theory

U2 - 10.1145/1840784.1840805

DO - 10.1145/1840784.1840805

M3 - Conference contribution

SN - 9781450302470

BT - nan

PB - Association for Computing Machinery

T2 - 3rd Information Interaction in Context Symposium

Y2 - 1 January 2010

ER -

Applying information foraging theory to understand user interaction with content-based image retrieval

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Keywords

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