Automatic relevance feedback for video retrieval

Authors:
P. Muneesawang;L. Guan
Affiliations:
Dept. of Electr. & Comput. Eng., Naresuan Univ., Phisanulok, Thailand;Dept. of Electr. Eng., IIT, Bombay, India
Venue:
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 1
Year:
2003

Citing 0
Cited 1

Learning ontology for personalized video retrieval

Workshop on multimedia information retrieval on The many faces of multimedia semantics

Quantified Score

Hi-index	0.00

Visualization

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.