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Titolo:
Learning similarity matching in multimedia content-based retrieval
Autore:
Lim, JH; Wu, JK; Singh, S; Narasimhalu, D;
Indirizzi:
Kent Ridge Digital Labs, Singapore 119613, Singapore Kent Ridge Digital Labs Singapore Singapore 119613 ore 119613, Singapore
Titolo Testata:
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
fascicolo: 5, volume: 13, anno: 2001,
pagine: 846 - 850
SICI:
1041-4347(200109/10)13:5<846:LSMIMC>2.0.ZU;2-H
Fonte:
ISI
Lingua:
ENG
Keywords:
content-based retrieval; image retrieval; multimedia databases; learning; ranking; similarity matching; relevance feedback;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Engineering, Computing & Technology
Citazioni:
7
Recensione:
Indirizzi per estratti:
Indirizzo: Lim, JH Kent Ridge Digital Labs, 21 Heng Mui Kent Terrace, Singapore 119613, Singapore Kent Ridge Digital Labs 21 Heng Mui Kent Terrace Singapore Singapore 119613
Citazione:
J.H. Lim et al., "Learning similarity matching in multimedia content-based retrieval", IEEE KNOWL, 13(5), 2001, pp. 846-850

Abstract

Many multimedia content-based retrieval systems allow query formulation with user setting of relative importance of features (e.g., color, texture, shape, etc) to mimic the user's perception of similarity. However, the systems do not modify their similarity matching functions, which are defined during the system development. In this paper, we present a neural network-based learning algorithm for adapting similarity matching function toward the user's query preference based on his/her relevance feedback. The relevance feedback is given as ranking errors (misranks) between the retrieved and desired lists of multimedia objects. The algorithm is demonstrated for facial image retrieval using the NIST Mugshot Identification Database with encouraging results.

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Documento generato il 12/07/20 alle ore 05:18:47