Dynamic factor graphs: A novel framework for multiple features data fusion
The Dynamic Tree [1] (DT) Bayesian Network is a powerful analytical tool for image segmentation and object segmentation tasks. Its hierarchical nature makes it possible to analyze and incorporate information from different scales, which is desirable in many applications. Having a flexible structure enables model selection, concurrent with parameter inference. In this paper, we propose a novel framework, dynamic factor graphs (DFG), where data segmentation and fusion tasks are combined in the same framework. Factor graphs (FGs) enable us to have a broader range of modeling applications than Bayesian networks (BNs) since FGs include both directed acyclic and undirected graphs in the same setting. The example in this paper will focus on segmentation and fusion of 2D image features with a linear Gaussian model assumption.