Semantic-Sensitive Classification for Large Image Library
Conference Proceeding Article
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.
Databases and Information Systems | Numerical Analysis and Scientific Computing
Data Management and Analytics
MMM 2005: Proceedings of the 11th International Multimedia Modelling Conference, 12-14 January, Melbourne, Australia (Poster Track)
City or Country
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. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/1236