Feature filtering in relevance feedback of image retrieval based on a statistical approach

Hong Fu; Zheru Chi; Dagan Feng

Feature filtering in relevance feedback of image retrieval based on a statistical approach

Hong Fu, Zheru Chi, Dagan Feng

The Hong Kong Polytechnic University

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

Abstract

Relevance feedback is a powerful tool to grasp the user's intention in image retrieval systems and has attracted many researchers' attention since 90's. In this paper, a feature filter whose parameters are computed by a statistical re-sampling approach is proposed in order to select the unique features to characterize the positive samples. A statistical voting procedure is then adopted to rank the candidates after getting rid of irrelevant feature components. Experiment results show that the proposed approach is more efficient and robust than the traditional method.

Original language	English
Title of host publication	2004 International Symposium on Intelligent Multimedia, Video and Speech Processing, ISIMP 2004
Pages	647-650
Number of pages	4
Publication status	Published - 1 Dec 2004
Event	2004 International Symposium on Intelligent Multimedia, Video and Speech Processing, ISIMP 2004 - Hong Kong, China, Hong Kong Duration: 20 Oct 2004 → 22 Oct 2004

Conference

Conference	2004 International Symposium on Intelligent Multimedia, Video and Speech Processing, ISIMP 2004
Country/Territory	Hong Kong
City	Hong Kong, China
Period	20/10/04 → 22/10/04

ASJC Scopus subject areas

General Engineering

Cite this

@inproceedings{de6dca6ef49e4dcbb4b5e88526a0fbbd,

title = "Feature filtering in relevance feedback of image retrieval based on a statistical approach",

abstract = "Relevance feedback is a powerful tool to grasp the user's intention in image retrieval systems and has attracted many researchers' attention since 90's. In this paper, a feature filter whose parameters are computed by a statistical re-sampling approach is proposed in order to select the unique features to characterize the positive samples. A statistical voting procedure is then adopted to rank the candidates after getting rid of irrelevant feature components. Experiment results show that the proposed approach is more efficient and robust than the traditional method.",

author = "Hong Fu and Zheru Chi and Dagan Feng",

year = "2004",

month = dec,

day = "1",

language = "English",

isbn = "0780386884",

pages = "647--650",

booktitle = "2004 International Symposium on Intelligent Multimedia, Video and Speech Processing, ISIMP 2004",

note = "2004 International Symposium on Intelligent Multimedia, Video and Speech Processing, ISIMP 2004 ; Conference date: 20-10-2004 Through 22-10-2004",

}

Fu, H, Chi, Z & Feng, D 2004, Feature filtering in relevance feedback of image retrieval based on a statistical approach. in 2004 International Symposium on Intelligent Multimedia, Video and Speech Processing, ISIMP 2004. pp. 647-650, 2004 International Symposium on Intelligent Multimedia, Video and Speech Processing, ISIMP 2004, Hong Kong, China, Hong Kong, 20/10/04.

Feature filtering in relevance feedback of image retrieval based on a statistical approach. / Fu, Hong; Chi, Zheru; Feng, Dagan.
2004 International Symposium on Intelligent Multimedia, Video and Speech Processing, ISIMP 2004. 2004. p. 647-650.

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

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AU - Chi, Zheru

AU - Feng, Dagan

PY - 2004/12/1

Y1 - 2004/12/1

N2 - Relevance feedback is a powerful tool to grasp the user's intention in image retrieval systems and has attracted many researchers' attention since 90's. In this paper, a feature filter whose parameters are computed by a statistical re-sampling approach is proposed in order to select the unique features to characterize the positive samples. A statistical voting procedure is then adopted to rank the candidates after getting rid of irrelevant feature components. Experiment results show that the proposed approach is more efficient and robust than the traditional method.

AB - Relevance feedback is a powerful tool to grasp the user's intention in image retrieval systems and has attracted many researchers' attention since 90's. In this paper, a feature filter whose parameters are computed by a statistical re-sampling approach is proposed in order to select the unique features to characterize the positive samples. A statistical voting procedure is then adopted to rank the candidates after getting rid of irrelevant feature components. Experiment results show that the proposed approach is more efficient and robust than the traditional method.

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M3 - Conference article published in proceeding or book

SN - 0780386884

SP - 647

EP - 650

BT - 2004 International Symposium on Intelligent Multimedia, Video and Speech Processing, ISIMP 2004

T2 - 2004 International Symposium on Intelligent Multimedia, Video and Speech Processing, ISIMP 2004

Y2 - 20 October 2004 through 22 October 2004

ER -

Feature filtering in relevance feedback of image retrieval based on a statistical approach

Abstract

Conference

ASJC Scopus subject areas

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