Learning a semantic space from user's relevance feedback for image retrieval

Xiaofei He; Oliver King; Wei-Ying Ma; Mingjing Li; Hong-Jiang Zhang

doi:10.1109/TCSVT.2002.808087

Learning a semantic space from user's relevance feedback for image retrieval

Xiaofei He
, Oliver King
, Wei-Ying Ma
, Mingjing Li
, Hong-Jiang Zhang

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

209 Citations (Scopus)

Abstract

As current methods for content-based retrieval are incapable of capturing the semantics of images, we experiment with using spectral methods to infer a semantic space from user's relevance feedback, so that our system will gradually improve its retrieval performance through accumulated user interactions. In addition to the long-term learning process, we also model the traditional approaches to query refinement using relevance feedback as a short-term learning process. The proposed short- and long-term learning frameworks have been integrated into an image retrieval system. Experimental results on a large collection of images have shown the effectiveness and robustness of our proposed algorithms.
© 2003 IEEE

Original language	English
Pages (from-to)	39-48
Journal	IEEE Transactions on Circuits and Systems for Video Technology
Volume	13
Issue number	1
DOIs	https://doi.org/10.1109/TCSVT.2002.808087
Publication status	Published - Jan 2003
Externally published	Yes

Bibliographical note

Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

Research Keywords

Image retrieval
Learning
Semantics
Singular value decomposition
User's relevance feedback

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AU - He, Xiaofei

AU - King, Oliver

AU - Ma, Wei-Ying

AU - Li, Mingjing

AU - Zhang, Hong-Jiang

N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

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N2 - As current methods for content-based retrieval are incapable of capturing the semantics of images, we experiment with using spectral methods to infer a semantic space from user's relevance feedback, so that our system will gradually improve its retrieval performance through accumulated user interactions. In addition to the long-term learning process, we also model the traditional approaches to query refinement using relevance feedback as a short-term learning process. The proposed short- and long-term learning frameworks have been integrated into an image retrieval system. Experimental results on a large collection of images have shown the effectiveness and robustness of our proposed algorithms. © 2003 IEEE

AB - As current methods for content-based retrieval are incapable of capturing the semantics of images, we experiment with using spectral methods to infer a semantic space from user's relevance feedback, so that our system will gradually improve its retrieval performance through accumulated user interactions. In addition to the long-term learning process, we also model the traditional approaches to query refinement using relevance feedback as a short-term learning process. The proposed short- and long-term learning frameworks have been integrated into an image retrieval system. Experimental results on a large collection of images have shown the effectiveness and robustness of our proposed algorithms. © 2003 IEEE

KW - Image retrieval

KW - Learning

KW - Semantics

KW - Singular value decomposition

KW - User's relevance feedback

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DO - 10.1109/TCSVT.2002.808087

M3 - RGC 21 - Publication in refereed journal

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Learning a semantic space from user's relevance feedback for image retrieval

Abstract

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Research Keywords

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