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

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

doi:10.1145/641007.641080

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

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

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

46 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 the 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.

Original language	English
Title of host publication	Proceedings of the ACM International Multimedia Conference and Exhibition
Publisher	Association for Computing Machinery
Pages	343-346
DOIs	https://doi.org/10.1145/641007.641080
Publication status	Published - 2002
Externally published	Yes
Event	10th International Conference of Multimedia - Juan les Pins, France Duration: 1 Dec 2002 → 6 Dec 2002

Conference

Conference	10th International Conference of Multimedia
Place	France
City	Juan les Pins
Period	1/12/02 → 6/12/02

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
User's relevance feedback

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10.1145/641007.641080

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Cite this

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title = "Learning and inferring a semantic space from user's relevance feedback for image retrieval",

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 the 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.",

keywords = "Image retrieval, Learning, User's relevance feedback",

author = "Xiaofei He and Wei-Ying Ma and Oliver King and Mingjing Li and Hongjiang Zhang",

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].; 10th International Conference of Multimedia ; Conference date: 01-12-2002 Through 06-12-2002",

year = "2002",

doi = "10.1145/641007.641080",

language = "English",

pages = "343--346",

booktitle = "Proceedings of the ACM International Multimedia Conference and Exhibition",

publisher = "Association for Computing Machinery",

address = "United States",

}

He, X, Ma, W-Y, King, O, Li, M & Zhang, H 2002, Learning and inferring a semantic space from user's relevance feedback for image retrieval. in Proceedings of the ACM International Multimedia Conference and Exhibition. Association for Computing Machinery, pp. 343-346, 10th International Conference of Multimedia, Juan les Pins, France, 1/12/02. https://doi.org/10.1145/641007.641080

Learning and inferring a semantic space from user's relevance feedback for image retrieval. / He, Xiaofei; Ma, Wei-Ying; King, Oliver et al.
Proceedings of the ACM International Multimedia Conference and Exhibition. Association for Computing Machinery, 2002. p. 343-346.

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Learning and inferring a semantic space from user's relevance feedback for image retrieval

AU - He, Xiaofei

AU - Ma, Wei-Ying

AU - King, Oliver

AU - Li, Mingjing

AU - Zhang, Hongjiang

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].

PY - 2002

Y1 - 2002

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 the 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.

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 the 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.

KW - Image retrieval

KW - Learning

KW - User's relevance feedback

UR - https://www.scopus.com/pages/publications/0037700825

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-0037700825&origin=recordpage

U2 - 10.1145/641007.641080

DO - 10.1145/641007.641080

M3 - RGC 32 - Refereed conference paper (with host publication)

SP - 343

EP - 346

BT - Proceedings of the ACM International Multimedia Conference and Exhibition

PB - Association for Computing Machinery

T2 - 10th International Conference of Multimedia

Y2 - 1 December 2002 through 6 December 2002

ER -

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

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Research Keywords

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