Automatic relevance feedback for video retrieval

P. Muneesawang; L. Guan

doi:10.1109/ICME.2003.1221631

Automatic relevance feedback for video retrieval

P. Muneesawang
, L. Guan

Naresuan University
Ryerson University

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

6 Citations (Scopus)

Abstract

This paper presents an automatic relevance feedback method for improving retrieval accuracy in video database. We first demonstrate a representation based on a template-frequency model (TFM) that allows the full use of the temporal dimension. We then integrate the TFM with a self-training neural network structure to adaptively capture different degrees of visual importance in a video sequence. Forward and backward signal propagation is the key in this automatic relevance feedback method in order to enhance retrieval accuracy.

Original language	English
Title of host publication	Proceedings - 2003 International Conference on Multimedia and Expo, ICME
Publisher	IEEE Computer Society
Pages	373-376
Number of pages	4
ISBN (Electronic)	0780379659
DOIs	https://doi.org/10.1109/ICME.2003.1221631
Publication status	Published - 2003
Externally published	Yes
Event	2003 International Conference on Multimedia and Expo, ICME 2003 - Baltimore, United States Duration: 6 Jul 2003 → 9 Jul 2003

Publication series

Name	Proceedings - IEEE International Conference on Multimedia and Expo
Volume	2
ISSN (Print)	1945-7871
ISSN (Electronic)	1945-788X

Conference

Conference	2003 International Conference on Multimedia and Expo, ICME 2003
Country/Territory	United States
City	Baltimore
Period	6/07/03 → 9/07/03

Access to Document

10.1109/ICME.2003.1221631

Cite this

@inproceedings{41a994da8fcb4cef97ae1117c27022c5,

title = "Automatic relevance feedback for video retrieval",

abstract = "This paper presents an automatic relevance feedback method for improving retrieval accuracy in video database. We first demonstrate a representation based on a template-frequency model (TFM) that allows the full use of the temporal dimension. We then integrate the TFM with a self-training neural network structure to adaptively capture different degrees of visual importance in a video sequence. Forward and backward signal propagation is the key in this automatic relevance feedback method in order to enhance retrieval accuracy.",

author = "P. Muneesawang and L. Guan",

note = "Publisher Copyright: {\textcopyright} 2003 IEEE.; 2003 International Conference on Multimedia and Expo, ICME 2003 ; Conference date: 06-07-2003 Through 09-07-2003",

year = "2003",

doi = "10.1109/ICME.2003.1221631",

language = "English",

series = "Proceedings - IEEE International Conference on Multimedia and Expo",

publisher = "IEEE Computer Society",

pages = "373--376",

booktitle = "Proceedings - 2003 International Conference on Multimedia and Expo, ICME",

}

Muneesawang, P & Guan, L 2003, Automatic relevance feedback for video retrieval. in Proceedings - 2003 International Conference on Multimedia and Expo, ICME., 1221631, Proceedings - IEEE International Conference on Multimedia and Expo, vol. 2, IEEE Computer Society, pp. 373-376, 2003 International Conference on Multimedia and Expo, ICME 2003, Baltimore, United States, 6/07/03. https://doi.org/10.1109/ICME.2003.1221631

Automatic relevance feedback for video retrieval. / Muneesawang, P.; Guan, L.
Proceedings - 2003 International Conference on Multimedia and Expo, ICME. IEEE Computer Society, 2003. p. 373-376 1221631 (Proceedings - IEEE International Conference on Multimedia and Expo; Vol. 2).

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

TY - GEN

T1 - Automatic relevance feedback for video retrieval

AU - Muneesawang, P.

AU - Guan, L.

PY - 2003

Y1 - 2003

N2 - This paper presents an automatic relevance feedback method for improving retrieval accuracy in video database. We first demonstrate a representation based on a template-frequency model (TFM) that allows the full use of the temporal dimension. We then integrate the TFM with a self-training neural network structure to adaptively capture different degrees of visual importance in a video sequence. Forward and backward signal propagation is the key in this automatic relevance feedback method in order to enhance retrieval accuracy.

AB - This paper presents an automatic relevance feedback method for improving retrieval accuracy in video database. We first demonstrate a representation based on a template-frequency model (TFM) that allows the full use of the temporal dimension. We then integrate the TFM with a self-training neural network structure to adaptively capture different degrees of visual importance in a video sequence. Forward and backward signal propagation is the key in this automatic relevance feedback method in order to enhance retrieval accuracy.

UR - https://www.scopus.com/pages/publications/84908604568

U2 - 10.1109/ICME.2003.1221631

DO - 10.1109/ICME.2003.1221631

M3 - Conference contribution

AN - SCOPUS:84908604568

T3 - Proceedings - IEEE International Conference on Multimedia and Expo

SP - 373

EP - 376

BT - Proceedings - 2003 International Conference on Multimedia and Expo, ICME

PB - IEEE Computer Society

T2 - 2003 International Conference on Multimedia and Expo, ICME 2003

Y2 - 6 July 2003 through 9 July 2003

ER -

Automatic relevance feedback for video retrieval

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