Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
4-2007
Abstract
One critical task in content-based video retrieval is to rank search results with combinations of multimodal resources effectively. This paper proposes a novel multimodal and multilevel ranking framework for content-based video retrieval. The main idea of our approach is to represent videos by graphs and learn harmonic ranking functions through fusing multimodal resources over these graphs smoothly. We further tackle the efficiency issue by a multilevel learning scheme, which makes the semi-supervised ranking method practical for large-scale applications. Our empirical evaluations on TRECVID 2005 dataset show that the proposed multimodal and multilevel ranking framework is effective and promising for content-based video retrieval.
Keywords
video retrieval, multimodal fusion, multilevel ranking, semi-supervised learning, performance evaluation
Discipline
Databases and Information Systems
Research Areas
Data Science and Engineering
Publication
IEEE International Conference on Acoustics, Speech and Signal Processing ICASSP 2007: Honolulu, Hawaii, 15-20 April 2007: Proceedings
First Page
1225
Last Page
1228
ISBN
9781424407286
Identifier
10.1109/ICASSP.2007.367297
Publisher
IEEE
City or Country
Piscataway, NJ
Citation
HOI, Steven C. H. and LYU, Michael R..
A multimodal and multilevel ranking framework for content-based video retrieval. (2007). IEEE International Conference on Acoustics, Speech and Signal Processing ICASSP 2007: Honolulu, Hawaii, 15-20 April 2007: Proceedings. 1225-1228.
Available at: https://ink.library.smu.edu.sg/sis_research/3944
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1109/ICASSP.2007.367297