MultiFusion: A Boosting Approach for Multimedia Fusion
The multimodal data usually contain complementary, correlated and redundant information. Thus, multimodal fusion is useful for many multisensor applications. Here, a novel multimodal fusion algorithm is proposed, which is referred to as "MultiFusion." The approach adopts a boosting structure where the atomic event is considered as the fusion unit. The correlation of multimodal data is used to form an overall classifier in each iteration. Moreover, by adopting the Adaboost-like structure, the overall fusion performance is improved. Both the simulation experiment and the real application show the effectiveness of the MultiFusion approach. Our approach can be applied in different multimodal applications to exploit the multimedia data characteristics and improve the performance.
boosting algorithms decision fusion atomic event multimodal fusion adaboost