Semantic-Sensitive Classification for Large Image Library
Publication Type
Conference Proceeding Article
Publication Date
2005
Abstract
With advances in multimedia technology, image data with various formats is is becoming available at an explosive rate from various domain applications. How to efficiently organise and access them has been an extremely important issue and enjoying growing attention. In this paper, we present results from experimental studies investigating performance of image classification for a novel dimension reduction scheme with hybrid architecture. We demonstrate that not only can the method provide superior quality of classification accuracy with various machine learning based classifier but also substantially speed up training and categorisation process. Moreover, it is fairly robust against various kinds of visual distortions and noises.
Discipline
Databases and Information Systems | Numerical Analysis and Scientific Computing
Publication
MMM 2005: Proceedings of the 11th International Multimedia Modelling Conference, 12-14 January, Melbourne, Australia (Poster Track)
First Page
340
Last Page
345
ISBN
9780769521640
Identifier
10.1109/MMMC.2005.66
Publisher
IEEE
City or Country
Melbourne, Australia
Citation
SHEN, Jialie; Shepherd, John; and Ngu, AHH.
Semantic-Sensitive Classification for Large Image Library. (2005). MMM 2005: Proceedings of the 11th International Multimedia Modelling Conference, 12-14 January, Melbourne, Australia (Poster Track). 340-345.
Available at: https://ink.library.smu.edu.sg/sis_research/1236
Additional URL
http://dx.doi.org/10.1109/MMMC.2005.66