Automatic relevance feedback for video retrieval

P. Muneesawang; L. Guan

Automatic relevance feedback for video retrieval

P. Muneesawang
, L. Guan

Naresuan University
Ryerson University

Research output: Contribution to journal › Conference article › peer-review

3 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
Pages (from-to)	1-4
Number of pages	4
Journal	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume	3
Publication status	Published - 2003
Externally published	Yes
Event	2003 IEEE International Conference on Accoustics, Speech, and Signal Processing - Hong Kong, Hong Kong Duration: 6 Apr 2003 → 10 Apr 2003

Cite this

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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",

year = "2003",

language = "English",

volume = "3",

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journal = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",

issn = "1520-6149",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

note = "2003 IEEE International Conference on Accoustics, Speech, and Signal Processing ; Conference date: 06-04-2003 Through 10-04-2003",

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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/0141788428

M3 - Conference article

AN - SCOPUS:0141788428

SN - 1520-6149

VL - 3

SP - 1

EP - 4

JO - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

JF - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

T2 - 2003 IEEE International Conference on Accoustics, Speech, and Signal Processing

Y2 - 6 April 2003 through 10 April 2003

ER -

Automatic relevance feedback for video retrieval

Abstract

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