Up-Fusion

Abstract

The amount of multimedia data on the Internet has increased exponentially in the past few decades and this trend is likely to continue. Multimedia content inherently has multiple information sources, therefore effective fusion methods are critical for data analysis and understanding. So far, most of the existing fusion methods are static with respect to time, making it difficult for them to handle the evolving multimedia content. To address this issue, in recent years, several evolving fusion methods were proposed, however, their requirements are difficult to meet, making them useful only in limited applications. In this article, we propose a novel evolving fusion method based on the online portfolio selection theory. The proposed method takes into account the correlation among different information sources and evolves the fusion model when new multimedia data is added. It performs effectively on both crisp and soft decisions without requiring additional context information. Extensive experiments on concept detection and human detection tasks over the TRECVID dataset and surveillance data have been conducted and significantly better performance has been obtained.