Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
7-2008
Abstract
This paper presents a self-organizing network model for the fusion of multimedia information. By synchronizing the encoding of information across multiple media channels, the neural model known as fusion Adaptive Resonance Theory (fusion ART) generates clusters that encode the associative mappings across multimedia information in a real-time and continuous manner. In addition, by incorporating a semantic category channel, fusion ART further enables multimedia information to be fused into predefined themes or semantic categories. We illustrate the fusion ART’s functionalities through experiments on two multimedia data sets in the terrorist domain and show the viability of the proposed approach.
Discipline
Databases and Information Systems
Research Areas
Data Science and Engineering
Publication
Proceedings of the 11th International Conference on Information Fusion, Cologne, Germany, June 30 - July 3
First Page
1738
Last Page
1744
Identifier
10.1109/ICIF.2008.4632421
Publisher
IEEE
City or Country
New York
Citation
NGUYEN, Luong-Dong; WOON, Kia-Yan; and TAN, Ah-hwee.
A self-organizing neural model for multimedia information fusion. (2008). Proceedings of the 11th International Conference on Information Fusion, Cologne, Germany, June 30 - July 3. 1738-1744.
Available at: https://ink.library.smu.edu.sg/sis_research/6796
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.