The fusion of multiple correlated observations of a multimedia system is a research problem arising in many multimedia applications. In this paper, we propose a novel framework for the probabilistic fusion of correlated multimedia observations. Assuming that each of the media stream has a priori probability of achieving the goal and their underlying correlations are known, our framework fuses the individual probabilities using the quantitative correlation based on a Bayesian approach. The simulation results show that fewer highly-positively-correlated observations better achieve a specified goal when compared to the use of a larger number of observations with low correlation.
Assuming that each media stream has an a priori probability of achieving the goal and that their underlying correlations are known, the assimilation framework in REF fuses the individual probabilities using quantitative correlation based on a Bayesian approach and a heuristic function.
Probability fusion for correlated multimedia streams
MULTIMEDIA '04
Computer Science