FA-MCADF: Feature Affinity Based Multiple Correspondence Analysis and Decision Fusion Framework for Disaster Information Management

Abstract

Multimedia semantic concept detection is one of the major research topics in multimedia data analysis in recent years. Disaster information management needs the assistance of multimedia data analysis to better utilize those disasterrelated information, which has been widely shared by people through the Internet. In this paper, a Feature Affinity based Multiple Correspondence Analysis and Decision Fusion (FAMCADF) framework is proposed to extract useful semantics from a disaster dataset. By utilizing the selected features and their affinities/ranks in each of the feature groups, the proposed framework is able to improve the concept detection results. Moreover, the decision fusion scheme further improves the accuracy performance. The experimental results demonstrate the effectiveness of the proposed framework and prove that the fusion of the decisions of the basic classifiers could make the framework outperform several existing approaches in the comparison.